text:
@article{Olivera-Nappa2011,
	author="Olivera-Nappa, Alvaro
	and Andrews, Barbara A.
	and Asenjo, Juan A.",
	title="Mutagenesis Objective Search and Selection Tool (MOSST): an algorithm to predict structure-function related mutations in proteins",
	journal="BMC Bioinformatics",
	year="2011",
	month="Apr",
	day="27",
	volume="12",
	number="1",
	pages="122",
	abstract="Functionally relevant artificial or natural mutations are difficult to assess or predict if no structure-function information is available for a protein. This is especially important to correctly identify functionally significant non-synonymous single nucleotide polymorphisms (nsSNPs) or to design a site-directed mutagenesis strategy for a target protein. A new and powerful methodology is proposed to guide these two decision strategies, based only on conservation rules of physicochemical properties of amino acids extracted from a multiple alignment of a protein family where the target protein belongs, with no need of explicit structure-function relationships.",
	issn="1471-2105",
	doi="10.1186/1471-2105-12-122",
	url="https://doi.org/10.1186/1471-2105-12-122"
}

@article{article,

	author = {Gossage, Lucy and Pires, Douglas and Olivera-Nappa, Alvaro and A. Asenjo, Juan and Bycroft, Mark and Blundell, Tom and Eisen, Tim},

	year = {2014},

	month = {06},

	pages = {},

	title = {An integrated computational approach can classify VHL missense mutations according to risk of clear cell Renal carcinoma},

	volume = {23},

	journal = {Human molecular genetics},

	doi = {10.1093/hmg/ddu321}

}

@Article{Capriotti2005,
	author={Capriotti, Emidio
	and Fariselli, Piero
	and Casadio, Rita},
	title={I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure},
	journal={Nucleic Acids Res},
	year={2005},
	month={Jul},
	day={01},
	publisher={Oxford University Press},
	volume={33},
	number={Web Server issue},
	pages={W306-W310},
	abstract={I-Mutant2.0 is a support vector machine (SVM)-based tool for the automatic prediction of protein stability changes upon single point mutations. I-Mutant2.0 predictions are performed starting either from the protein structure or, more importantly, from the protein sequence. This latter task, to the best of our knowledge, is exploited for the first time. The method was trained and tested on a data set derived from ProTherm, which is presently the most comprehensive available database of thermodynamic experimental data of free energy changes of protein stability upon mutation under different conditions. I-Mutant2.0 can be used both as a classifier for predicting the sign of the protein stability change upon mutation and as a regression estimator for predicting the related DeltaDeltaG values. Acting as a classifier, I-Mutant2.0 correctly predicts (with a cross-validation procedure) 80\% or 77\% of the data set, depending on the usage of structural or sequence information, respectively. When predicting DeltaDeltaG values associated with mutations, the correlation of predicted with expected/experimental values is 0.71 (with a standard error of 1.30 kcal/mol) and 0.62 (with a standard error of 1.45 kcal/mol) when structural or sequence information are respectively adopted. Our web interface allows the selection of a predictive mode that depends on the availability of the protein structure and/or sequence. In this latter case, the web server requires only pasting of a protein sequence in a raw format. We therefore introduce I-Mutant2.0 as a unique and valuable helper for protein design, even when the protein structure is not yet known with atomic resolution. AVAILABILITY: http://gpcr.biocomp.unibo.it/cgi/predictors/I-Mutant2.0/I-Mutant2.0.cgi.},
	note={15980478[pmid]},
	note={PMC1160136[pmcid]},
	note={33/suppl\_2/W306[PII]},
	issn={1362-4962},
	doi={10.1093/nar/gki375},
	url={https://www.ncbi.nlm.nih.gov/pubmed/15980478}
}

@Article{Quan2016,
	author={Quan, Lijun
	and Lv, Qiang
	and Zhang, Yang},
	title={STRUM: structure-based prediction of protein stability changes upon single-point mutation},
	journal={Bioinformatics},
	year={2016},
	month={Oct},
	day={01},
	publisher={Oxford University Press},
	volume={32},
	number={19},
	pages={2936-2946},
	abstract={MOTIVATION: Mutations in human genome are mainly through single nucleotide polymorphism, some of which can affect stability and function of proteins, causing human diseases. Several methods have been proposed to predict the effect of mutations on protein stability; but most require features from experimental structure. Given the fast progress in protein structure prediction, this work explores the possibility to improve the mutation-induced stability change prediction using low-resolution structure modeling. RESULTS: We developed a new method (STRUM) for predicting stability change caused by single-point mutations. Starting from wild-type sequences, 3D models are constructed by the iterative threading assembly refinement (I-TASSER) simulations, where physics- and knowledge-based energy functions are derived on the I-TASSER models and used to train STRUM models through gradient boosting regression. STRUM was assessed by 5-fold cross validation on 3421 experimentally determined mutations from 150 proteins. The Pearson correlation coefficient (PCC) between predicted and measured changes of Gibbs free-energy gap, DDG, upon mutation reaches 0.79 with a root-mean-square error 1.2 kcal/mol in the mutation-based cross-validations. The PCC reduces if separating training and test mutations from non-homologous proteins, which reflects inherent correlations in the current mutation sample. Nevertheless, the results significantly outperform other state-of-the-art methods, including those built on experimental protein structures. Detailed analyses show that the most sensitive features in STRUM are the physics-based energy terms on I-TASSER models and the conservation scores from multiple-threading template alignments. However, the DDG prediction accuracy has only a marginal dependence on the accuracy of protein structure models as long as the global fold is correct. These data demonstrate the feasibility to use low-resolution structure modeling for high-accuracy stability change prediction upon point mutations. AVAILABILITY AND IMPLEMENTATION: http://zhanglab.ccmb.med.umich.edu/STRUM/ CONTACT: qiang@suda.edu.cn and zhng@umich.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.},
	note={27318206[pmid]},
	note={PMC5039926[pmcid]},
	note={btw361[PII]},
	issn={1367-4811},
	doi={10.1093/bioinformatics/btw361},
	url={https://www.ncbi.nlm.nih.gov/pubmed/27318206}
}

@Article{Capriotti2008,
	author={Capriotti, Emidio
	and Fariselli, Piero
	and Rossi, Ivan
	and Casadio, Rita},
	title={A three-state prediction of single point mutations on protein stability changes},
	journal={BMC Bioinformatics},
	year={2008},
	volume={9},
	number={2},
	pages={S6},
	abstract={A basic question of protein structural studies is to which extent mutations affect the stability. This question may be addressed starting from sequence and/or from structure. In proteomics and genomics studies prediction of protein stability free energy change (DDG) upon single point mutation may also help the annotation process. The experimental DDG values are affected by uncertainty as measured by standard deviations. Most of the DDG values are nearly zero (about 32\% of the DDG data set ranges from -0.5 to 0.5 kcal/mole) and both the value and sign of DDG may be either positive or negative for the same mutation blurring the relationship among mutations and expected DDG value. In order to overcome this problem we describe a new predictor that discriminates between 3 mutation classes: destabilizing mutations (DDG<-1.0 kcal/mol), stabilizing mutations (DDG>1.0 kcal/mole) and neutral mutations (-1.0<=DDG<=1.0 kcal/mole).},
	issn={1471-2105},
	doi={10.1186/1471-2105-9-S2-S6},
	url={https://doi.org/10.1186/1471-2105-9-S2-S6}
}

@Article{Broom2017,
	author={Broom, Aron
	and Jacobi, Zachary
	and Trainor, Kyle
	and Meiering, Elizabeth M.},
	title={Computational tools help improve protein stability but with a solubility tradeoff},
	journal={J Biol Chem},
	year={2017},
	month={Sep},
	day={01},
	publisher={American Society for Biochemistry and Molecular Biology},
	volume={292},
	number={35},
	pages={14349-14361},
	abstract={Accurately predicting changes in protein stability upon amino acid substitution is a much sought after goal. Destabilizing mutations are often implicated in disease, whereas stabilizing mutations are of great value for industrial and therapeutic biotechnology. Increasing protein stability is an especially challenging task, with random substitution yielding stabilizing mutations in only {\textasciitilde}2\% of cases. To overcome this bottleneck, computational tools that aim to predict the effect of mutations have been developed; however, achieving accuracy and consistency remains challenging. Here, we combined 11 freely available tools into a meta-predictor (meieringlab.uwaterloo.ca/stabilitypredict/). Validation against {\textasciitilde}600 experimental mutations indicated that our meta-predictor has improved performance over any of the individual tools. The meta-predictor was then used to recommend 10 mutations in a previously designed protein of moderate thermodynamic stability, ThreeFoil. Experimental characterization showed that four mutations increased protein stability and could be amplified through ThreeFoil's structural symmetry to yield several multiple mutants with >2-kcal/mol stabilization. By avoiding residues within functional ties, we could maintain ThreeFoil's glycan-binding capacity. Despite successfully achieving substantial stabilization, however, almost all mutations decreased protein solubility, the most common cause of protein design failure. Examination of the 600-mutation data set revealed that stabilizing mutations on the protein surface tend to increase hydrophobicity and that the individual tools favor this approach to gain stability. Thus, whereas currently available tools can increase protein stability and combining them into a meta-predictor yields enhanced reliability, improvements to the potentials/force fields underlying these tools are needed to avoid gaining protein stability at the cost of solubility.},
	note={28710274[pmid]},
	note={PMC5582830[pmcid]},
	note={M117.784165[PII]},
	issn={1083-351X},
	doi={10.1074/jbc.M117.784165},
	url={https://www.ncbi.nlm.nih.gov/pubmed/28710274}
}

@Article{Schymkowitz2005,
	author={Schymkowitz, Joost
	and Borg, Jesper
	and Stricher, Francois
	and Nys, Robby
	and Rousseau, Frederic
	and Serrano, Luis},
	title={The FoldX web server: an online force field},
	journal={Nucleic Acids Res},
	year={2005},
	month={Jul},
	day={01},
	publisher={Oxford University Press},
	volume={33},
	number={Web Server issue},
	pages={W382-W388},
	abstract={FoldX is an empirical force field that was developed for the rapid evaluation of the effect of mutations on the stability, folding and dynamics of proteins and nucleic acids. The core functionality of FoldX, namely the calculation of the free energy of a macromolecule based on its high-resolution 3D structure, is now publicly available through a web server at http://foldx.embl.de/. The current release allows the calculation of the stability of a protein, calculation of the positions of the protons and the prediction of water bridges, prediction of metal binding sites and the analysis of the free energy of complex formation. Alanine scanning, the systematic truncation of side chains to alanine, is also included. In addition, some reporting functions have been added, and it is now possible to print both the atomic interaction networks that constitute the protein, print the structural and energetic details of the interactions per atom or per residue, as well as generate a general quality report of the pdb structure. This core functionality will be further extended as more FoldX applications are developed.},
	note={15980494[pmid]},
	note={PMC1160148[pmcid]},
	note={33/suppl\_2/W382[PII]},
	issn={1362-4962},
	doi={10.1093/nar/gki387},
	url={https://www.ncbi.nlm.nih.gov/pubmed/15980494}
}

@Article{Pandurangan2017,
	author={Pandurangan, Arun Prasad
	and Ochoa-Monta{\~n}o, Bernardo
	and Ascher, David B.
	and Blundell, Tom L.},
	title={SDM: a server for predicting effects of mutations on protein stability},
	journal={Nucleic Acids Res},
	year={2017},
	month={Jul},
	day={03},
	publisher={Oxford University Press},
	volume={45},
	number={W1},
	pages={W229-W235},
	abstract={Here, we report a webserver for the improved SDM, used for predicting the effects of mutations on protein stability. As a pioneering knowledge-based approach, SDM has been highlighted as the most appropriate method to use in combination with many other approaches. We have updated the environment-specific amino-acid substitution tables based on the current expanded PDB (a 5-fold increase in information), and introduced new residue-conformation and interaction parameters, including packing density and residue depth. The updated server has been extensively tested using a benchmark containing 2690 point mutations from 132 different protein structures. The revised method correlates well against the hypothetical reverse mutations, better than comparable methods built using machine-learning approaches, highlighting the strength of our knowledge-based approach for identifying stabilising mutations. Given a PDB file (a Protein Data Bank file format containing the 3D coordinates of the protein atoms), and a point mutation, the server calculates the stability difference score between the wildtype and mutant protein. The server is available at http://structure.bioc.cam.ac.uk/sdm2.},
	note={28525590[pmid]},
	note={PMC5793720[pmcid]},
	note={3835311[PII]},
	issn={1362-4962},
	doi={10.1093/nar/gkx439},
	url={https://www.ncbi.nlm.nih.gov/pubmed/28525590}
}

@incollection{rohl2004protein,
	title={Protein structure prediction using Rosetta},
	author={Rohl, Carol A and Strauss, Charlie EM and Misura, Kira MS and Baker, David},
	booktitle={Methods in enzymology},
	volume={383},
	pages={66--93},
	year={2004},
	publisher={Elsevier}
}

@Article{Khan2010,
	author={Khan, Sofia
	and Vihinen, Mauno},
	title={Performance of protein stability predictors},
	journal={Human Mutation},
	year={2010},
	month={Jun},
	day={01},
	publisher={John Wiley \& Sons, Ltd},
	volume={31},
	number={6},
	pages={675-684},
	keywords={protein stability},
	keywords={free energy},
	keywords={missense mutations},
	keywords={stability predictors},
	keywords={prediction programs},
	keywords={bioinformatics},
	keywords={computational methods},
	keywords={predictions},
	abstract={Abstract Stability is a fundamental property affecting function, activity, and regulation of biomolecules. Stability changes are often found for mutated proteins involved in diseases. Stability predictors computationally predict protein?stability changes caused by mutations. We performed a systematic analysis of 11 online stability predictors' performances. These predictors are CUPSAT, Dmutant, FoldX, I-Mutant2.0, two versions of I-Mutant3.0 (sequence and structure versions), MultiMutate, MUpro, SCide, Scpred, and SRide. As input, 1,784 single mutations found in 80 proteins were used, and these mutations did not include those used for training. The programs' performances were also assessed according to where the mutations were found in the proteins, that is, in secondary structures and on the surface or in the core of a protein, and according to protein structure type. The extents to which the mutations altered the occupied volumes at the residue sites and the charge interactions were also characterized. The predictions of all programs were in line with the experimental data. I-Mutant3.0 (utilizing structural information), Dmutant, and FoldX were the most reliable predictors. The stability-center predictors performed with similar accuracy. However, at best, the predictions were only moderately accurate (?60\%) and significantly better tools would be needed for routine analysis of mutation effects. Hum Mutat 31:1?10, 2010. ? 2010 Wiley-Liss, Inc.},
	issn={1059-7794},
	doi={10.1002/humu.21242},
	url={https://doi.org/10.1002/humu.21242}
}

@Article{Parthiban2006,
	author={Parthiban, Vijaya
	and Gromiha, M. Michael
	and Schomburg, Dietmar},
	title={CUPSAT: prediction of protein stability upon point mutations},
	journal={Nucleic Acids Res},
	year={2006},
	month={Jul},
	day={01},
	publisher={Oxford University Press},
	volume={34},
	number={Web Server issue},
	pages={W239-W242},
	abstract={CUPSAT (Cologne University Protein Stability Analysis Tool) is a web tool to analyse and predict protein stability changes upon point mutations (single amino acid mutations). This program uses structural environment specific atom potentials and torsion angle potentials to predict DeltaDeltaG, the difference in free energy of unfolding between wild-type and mutant proteins. It requires the protein structure in Protein Data Bank format and the location of the residue to be mutated. The output consists information about mutation site, its structural features (solvent accessibility, secondary structure and torsion angles), and comprehensive information about changes in protein stability for 19 possible substitutions of a specific amino acid mutation. Additionally, it also analyses the ability of the mutated amino acids to adapt the observed torsion angles. Results were tested on 1538 mutations from thermal denaturation and 1603 mutations from chemical denaturation experiments. Several validation tests (split-sample, jack-knife and k-fold) were carried out to ensure the reliability, accuracy and transferability of the prediction method that gives >80\% prediction accuracy for most of these validation tests. Thus, the program serves as a valuable tool for the analysis of protein design and stability. The tool is accessible from the link http://cupsat.uni-koeln.de.},
	note={16845001[pmid]},
	note={PMC1538884[pmcid]},
	note={34/suppl\_2/W239[PII]},
	issn={1362-4962},
	doi={10.1093/nar/gkl190},
	url={https://www.ncbi.nlm.nih.gov/pubmed/16845001}
}

@article{vaisman,

	author = {Vaisman, Iosif I. and Masso, Majid},

	title = "{Accurate prediction of stability changes in protein mutants by combining machine learning with structure based computational mutagenesis}",

	journal = {Bioinformatics},

	volume = {24},

	number = {18},

	pages = {2002-2009},

	year = {2008},

	month = {07},

	abstract = "{Motivation: Accurate predictive models for the impact of single amino acid substitutions on protein stability provide insight into protein structure and function. Such models are also valuable for the design and engineering of new proteins. Previously described methods have utilized properties of protein sequence or structure to predict the free energy change of mutants due to thermal (ΔΔG) and denaturant (ΔΔGH2O) denaturations, as well as mutant thermal stability (ΔTm), through the application of either computational energy-based approaches or machine learning techniques. However, accuracy associated with applying these methods separately is frequently far from optimal.Results: We detail a computational mutagenesis technique based on a four-body, knowledge-based, statistical contact potential. For any mutation due to a single amino acid replacement in a protein, the method provides an empirical normalized measure of the ensuing environmental perturbation occurring at every residue position. A feature vector is generated for the mutant by considering perturbations at the mutated position and it's ordered six nearest neighbors in the 3-dimensional (3D) protein structure. These predictors of stability change are evaluated by applying machine learning tools to large training sets of mutants derived from diverse proteins that have been experimentally studied and described. Predictive models based on our combined approach are either comparable to, or in many cases significantly outperform, previously published results.Availability: A web server with supporting documentation is available at http://proteins.gmu.edu/automuteContact:ivaisman@gmu.edu}",

	issn = {1367-4803},

	doi = {10.1093/bioinformatics/btn353},

	url = {https://doi.org/10.1093/bioinformatics/btn353},

	eprint = {http://oup.prod.sis.lan/bioinformatics/article-pdf/24/18/2002/471405/btn353.pdf},

}

@Article{Getov2016,
	author={Getov, Ivan
	and Petukh, Marharyta
	and Alexov, Emil},
	title={SAAFEC: Predicting the Effect of Single Point Mutations on Protein Folding Free Energy Using a Knowledge-Modified MM/PBSA Approach},
	journal={Int J Mol Sci},
	year={2016},
	month={Apr},
	day={07},
	publisher={MDPI},
	volume={17},
	number={4},
	pages={512-512},
	abstract={Folding free energy is an important biophysical characteristic of proteins that reflects the overall stability of the 3D structure of macromolecules. Changes in the amino acid sequence, naturally occurring or made in vitro, may affect the stability of the corresponding protein and thus could be associated with disease. Several approaches that predict the changes of the folding free energy caused by mutations have been proposed, but there is no method that is clearly superior to the others. The optimal goal is not only to accurately predict the folding free energy changes, but also to characterize the structural changes induced by mutations and the physical nature of the predicted folding free energy changes. Here we report a new method to predict the Single Amino Acid Folding free Energy Changes (SAAFEC) based on a knowledge-modified Molecular Mechanics Poisson-Boltzmann (MM/PBSA) approach. The method is comprised of two main components: a MM/PBSA component and a set of knowledge based terms delivered from a statistical study of the biophysical characteristics of proteins. The predictor utilizes a multiple linear regression model with weighted coefficients of various terms optimized against a set of experimental data. The aforementioned approach yields a correlation coefficient of 0.65 when benchmarked against 983 cases from 42 proteins in the ProTherm database. AVAILABILITY: the webserver can be accessed via http://compbio.clemson.edu/SAAFEC/.},
	note={27070572[pmid]},
	note={PMC4848968[pmcid]},
	issn={1422-0067},
	doi={10.3390/ijms17040512},
	url={https://www.ncbi.nlm.nih.gov/pubmed/27070572}
}

@article{michie1994machine,
	title={Machine learning},
	author={Michie, Donald and Spiegelhalter, David J and Taylor, CC and others},
	journal={Neural and Statistical Classification},
	volume={13},
	year={1994},
	publisher={Technometrics}
}

@ARTICLE{7912315,
	author={M. {Chen} and Y. {Hao} and K. {Hwang} and L. {Wang} and L. {Wang}},
	journal={IEEE Access},
	title={Disease Prediction by Machine Learning Over Big Data From Healthcare Communities},
	year={2017},
	volume={5},
	number={},
	pages={8869-8879},
	keywords={Big Data;health care;learning (artificial intelligence);neural nets;disease prediction;healthcare communities;machine learning algorithms;real-life hospital data;convolutional neural network-based multimodal disease risk prediction algorithm;CNN-based multimodal disease risk prediction algorithm;medical big data analytics;Diseases;Hospitals;Prediction algorithms;Machine learning algorithms;Big Data;Data models;Big data analytics;machine learning;healthcare},
	doi={10.1109/ACCESS.2017.2694446},
	ISSN={2169-3536},
	month={},
}

@article{ABDELAZIZ2018117,
	title = "A machine learning model for improving healthcare services on cloud computing environment",
	journal = "Measurement",
	volume = "119",
	pages = "117 - 128",
	year = "2018",
	issn = "0263-2241",
	doi = "https://doi.org/10.1016/j.measurement.2018.01.022",
	url = "http://www.sciencedirect.com/science/article/pii/S0263224118300228",
	author = "Ahmed Abdelaziz and Mohamed Elhoseny and Ahmed S. Salama and A.M. Riad",
	keywords = "Cloud computing, Health services, Parallel particle swarm optimization, Linear regression, Neural network, Chronic kidney disease",
	abstract = "Recently, cloud computing gained an important role in healthcare services (HCS) due to its ability to improve the HCS performance. However, the optimal selection of virtual machines (VMs) to process a medical request represents a big challenge. Optimal selection of VMs performs a significant enhancement of the performance through reducing the execution time of medical requests (tasks) coming from stakeholders (patients, doctors, etc.) and maximizing utilization of cloud resources. For that, this paper proposes a new model for HCS based on cloud environment using Parallel Particle Swarm Optimization (PPSO) to optimize the VMs selection. In addition, a new model for chronic kidney disease (CKD) diagnosis and prediction is proposed to measure the performance of our VMs model. The prediction model of CKD is implemented using two consecutive techniques, which are linear regression (LR) and neural network (NN). LR is used to determine critical factors that influence on CKD. NN is used to predict of CKD. The results show that, the proposed model outperforms the state-of-the art models in total execution time the rate of 50%. In addition, the system efficiency regarding real-time data retrieval is greatly improved by 5.2%. In addition, the accuracy of hybrid intelligent model in predicting of CKD is 97.8%. The proposed model is superior to most of the referred models in the related works by 64%."
}

@inproceedings{cooley1997web,
	title={Web Mining: Information and Pattern Discovery on the World Wide Web.},
	author={Cooley, Robert and Mobasher, Bamshad and Srivastava, Jaideep and others},
	booktitle={ictai},
	volume={97},
	pages={558--567},
	year={1997}
}

@Article{juanito,
	author={Larra{\~n}aga, Pedro
	and Calvo, Borja
	and Santana, Roberto
	and Bielza, Concha
	and Galdiano, Josu
	and Inza, I{\~n}aki
	and Lozano, Jos{\'e} A.
	and Arma{\~n}anzas, Rub{\'e}n
	and Santaf{\'e}, Guzm{\'a}n
	and P{\'e}rez, Aritz
	and Robles, Victor},
	title={Machine learning in bioinformatics},
	journal={Briefings in Bioinformatics},
	year={2006},
	month={Mar},
	day={01},
	volume={7},
	number={1},
	pages={86-112},
	abstract={This article reviews machine learning methods for bioinformatics. It presents modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization. Applications in genomics, proteomics, systems biology, evolution and text mining are also shown.},
	issn={1467-5463},
	doi={10.1093/bib/bbk007},
	url={https://doi.org/10.1093/bib/bbk007}
}

@InProceedings{imageA,
	author="Duygulu, P.
	and Barnard, K.
	and de Freitas, J. F. G.
	and Forsyth, D. A.",
	editor="Heyden, Anders
	and Sparr, Gunnar
	and Nielsen, Mads
	and Johansen, Peter",
	title="Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary",
	booktitle="Computer Vision --- ECCV 2002",
	year="2002",
	publisher="Springer Berlin Heidelberg",
	address="Berlin, Heidelberg",
	pages="97--112",
	abstract="We describe a model of object recognition as machine translation. In this model, recognition is a process of annotating image regions with words. Firstly, images are segmented into regions, which are classified into region types using a variety of features. A mapping between region types and keywords supplied with the images, is then learned, using a method based around EM. This process is analogous with learning a lexicon from an aligned bitext. For the implementation we describe, these words are nouns taken from a large vocabulary. On a large test set, the method can predict numerous words with high accuracy. Simple methods identify words that cannot be predicted well. We show how to cluster words that individually are difficult to predict into clusters that can be predicted well --- for example, we cannot predict the distinction between train and locomotive using the current set of features, but we can predict the underlying concept. The method is trained on a substantial collection of images. Extensive experimental results illustrate the strengths and weaknesses of the approach.",
	isbn="978-3-540-47979-6"
}

@article{netzer2011reading,
	title={Reading digits in natural images with unsupervised feature learning},
	author={Netzer, Yuval and Wang, Tao and Coates, Adam and Bissacco, Alessandro and Wu, Bo and Ng, Andrew Y},
	year={2011}
}

@inproceedings{alm2005emotions,
	title={Emotions from text: machine learning for text-based emotion prediction},
	author={Alm, Cecilia Ovesdotter and Roth, Dan and Sproat, Richard},
	booktitle={Proceedings of the conference on human language technology and empirical methods in natural language processing},
	pages={579--586},
	year={2005},
	organization={Association for Computational Linguistics}
}

@ARTICLE{6313426,
	author={J. M. {Keller} and M. R. {Gray} and J. A. {Givens}},
	journal={IEEE Transactions on Systems, Man, and Cybernetics},
	title={A fuzzy K-nearest neighbor algorithm},
	year={1985},
	volume={SMC-15},
	number={4},
	pages={580-585},
	keywords={Bayes methods;fuzzy set theory;pattern recognition;K-nearest neighbor decision rule;Bayes decision;classification;pattern recognition;fuzzy sets;fuzzy memberships;labeled samples;Support vector machine classification;Classification algorithms;Prototypes;Vectors;Iris;Error analysis;Pattern recognition},
	doi={10.1109/TSMC.1985.6313426},
	ISSN={0018-9472},
	month={July},
}

@ARTICLE{5408784,
	author={S. A. {Dudani}},
	journal={IEEE Transactions on Systems, Man, and Cybernetics},
	title={The Distance-Weighted k-Nearest-Neighbor Rule},
	year={1976},
	volume={SMC-6},
	number={4},
	pages={325-327},
	keywords={Nearest neighbor searches;Error correction;Upper bound;H infinity control},
	doi={10.1109/TSMC.1976.5408784},
	ISSN={0018-9472},
	month={April},
}

@article{DANIELSSON1980227,
	title = "Euclidean distance mapping",
	journal = "Computer Graphics and Image Processing",
	volume = "14",
	number = "3",
	pages = "227 - 248",
	year = "1980",
	issn = "0146-664X",
	doi = "https://doi.org/10.1016/0146-664X(80)90054-4",
	url = "http://www.sciencedirect.com/science/article/pii/0146664X80900544",
	author = "Per-Erik Danielsson",
	abstract = "Based on a two-component descriptor, a distance label for each point, it is shown that Euclidean distance maps can be generated by effective sequential algorithms. The map indicates, for each pixel in the objects (or the background) of the originally binary picture, the shortest distance to the nearest pixel in the background (or the objects). A map with negligible errors can be produced in two picture scans which has to include forward and backward movement for each line. Thus, for expanding/shrinking purposes it may compete very successfully with iterative parallel propagation in the binary picture itself. It is shown that skeletons can be produced by simple procedures and since these are based on Euclidean distances it is assumed that they are superior to skeletons based on d4−, d8−, and even octagonal metrics."
}

@article{PERLIBAKAS2004711,
	title = "Distance measures for PCA-based face recognition",
	journal = "Pattern Recognition Letters",
	volume = "25",
	number = "6",
	pages = "711 - 724",
	year = "2004",
	issn = "0167-8655",
	doi = "https://doi.org/10.1016/j.patrec.2004.01.011",
	url = "http://www.sciencedirect.com/science/article/pii/S0167865504000248",
	author = "Vytautas Perlibakas",
	keywords = "Face recognition, PCA, Distance measures",
	abstract = "In this article we compare 14 distance measures and their modifications between feature vectors with respect to the recognition performance of the principal component analysis (PCA)-based face recognition method and propose modified sum square error (SSE)-based distance. Recognition experiments were performed using the database containing photographies of 423 persons. The experiments showed, that the proposed distance measure was among the first three best measures with respect to different characteristics of the biometric systems. The best recognition results were achieved using the following distance measures: simplified Mahalanobis, weighted angle-based distance, proposed modified SSE-based distance, angle-based distance between whitened feature vectors. Using modified SSE-based distance we need to extract less images in order to achieve 100% cumulative recognition than using any other tested distance measure. We also showed that using the algorithmic combination of distance measures we can achieve better recognition results than using the distances separately."
}

@article{LIAO20155328,
	title = "Approaches to manage hesitant fuzzy linguistic information based on the cosine distance and similarity measures for HFLTSs and their application in qualitative decision making",
	journal = "Expert Systems with Applications",
	volume = "42",
	number = "12",
	pages = "5328 - 5336",
	year = "2015",
	issn = "0957-4174",
	doi = "https://doi.org/10.1016/j.eswa.2015.02.017",
	url = "http://www.sciencedirect.com/science/article/pii/S0957417415001165",
	author = "Huchang Liao and Zeshui Xu",
	keywords = "Hesitant fuzzy linguistic term set, Hesitant fuzzy linguistic cosine distance measure, Hesitant fuzzy linguistic cosine similarity measure, Multiple criteria decision making, Cosine-distance-based HFL-TOPSIS method, Cosine-distance-based HFL-VIKOR method",
	abstract = "Qualitative and hesitant information is common in practical decision making process. In such complicated decision making problem, it is flexible for experts to use comparative linguistic expressions to express their opinions since the linguistic expressions are much closer than single or simple linguistic term to human way of thinking and cognition. The hesitant fuzzy linguistic term set (HFLTS) turns out to be a powerful tool in representing and eliciting the comparative linguistic expressions. In order to develop some approaches to decision making with hesitant fuzzy linguistic information, in this paper, we firstly introduce a family of novel distance and similarity measures for HFLTSs, such as the cosine distance and similarity measures, the weighted cosine distance and similarity measures, the order weighted cosine distance and similarity measures, and the continuous cosine distance and similarity measures. All these distance and similarity measures are proposed from the geometric point of view while the existing distance and similarity measures over HFLTSs are based on the different forms of algebra distance measures. Afterwards, based on the hesitant fuzzy linguistic cosine distance measures between hesitant fuzzy linguistic elements, the cosine-distance-based HFL-TOPSIS method and the cosine-distance-based HFL-VIKOR method are developed to dealing with hesitant fuzzy linguistic multiple criteria decision making problems. The step by step algorithms of these two methods are given for the convenience of applications. Finally, a numerical example concerning the selection of ERP systems is given to illustrate the validation and efficiency of the proposed methods."
}

@article{DEMAESSCHALCK20001,
	title = "The Mahalanobis distance",
	journal = "Chemometrics and Intelligent Laboratory Systems",
	volume = "50",
	number = "1",
	pages = "1 - 18",
	year = "2000",
	issn = "0169-7439",
	doi = "https://doi.org/10.1016/S0169-7439(99)00047-7",
	url = "http://www.sciencedirect.com/science/article/pii/S0169743999000477",
	author = "R. De Maesschalck and D. Jouan-Rimbaud and D.L. Massart",
	keywords = "Mahalanobis distance, Euclidean distance, Principal components",
	abstract = "The theory of many multivariate chemometrical methods is based on the measurement of distances. The Mahalanobis distance (MD), in the original and principal component (PC) space, will be examined and interpreted in relation with the Euclidean distance (ED). Techniques based on the MD and applied in different fields of chemometrics such as in multivariate calibration, pattern recognition and process control are explained and discussed."
}

@article{pedregosa2011scikit,
	title={Scikit-learn: Machine learning in Python},
	author={Pedregosa, Fabian and Varoquaux, Ga{\"e}l and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and others},
	journal={Journal of machine learning research},
	volume={12},
	number={Oct},
	pages={2825--2830},
	year={2011}
}

@article{TAN2005667,
	title = "Neighbor-weighted K-nearest neighbor for unbalanced text corpus",
	journal = "Expert Systems with Applications",
	volume = "28",
	number = "4",
	pages = "667 - 671",
	year = "2005",
	issn = "0957-4174",
	doi = "https://doi.org/10.1016/j.eswa.2004.12.023",
	url = "http://www.sciencedirect.com/science/article/pii/S0957417404001708",
	author = "Songbo Tan",
	keywords = "Text classification, K-Nearest neighbor (KNN), Information retrieval, Data mining",
	abstract = "Text categorization or classification is the automated assigning of text documents to pre-defined classes based on their contents. Many of classification algorithms usually assume that the training examples are evenly distributed among different classes. However, unbalanced data sets often appear in many practical applications. In order to deal with uneven text sets, we propose the neighbor-weighted K-nearest neighbor algorithm, i.e. NWKNN. The experimental results indicate that our algorithm NWKNN achieves significant classification performance improvement on imbalanced corpora."
}

@article{zhang2004optimality,
	title={The optimality of naive Bayes},
	author={Zhang, Harry},
	journal={AA},
	volume={1},
	number={2},
	pages={3},
	year={2004}
}

@article{manning2010introduction,
	title={Introduction to information retrieval},
	author={Manning, Christopher and Raghavan, Prabhakar and Sch{\"u}tze, Hinrich},
	journal={Natural Language Engineering},
	volume={16},
	number={1},
	pages={100--103},
	year={2010},
	publisher={Cambridge university press}
}

@inproceedings{metsis2006spam,
	title={Spam filtering with naive bayes-which naive bayes?},
	author={Metsis, Vangelis and Androutsopoulos, Ion and Paliouras, Georgios},
	booktitle={CEAS},
	volume={17},
	pages={28--69},
	year={2006},
	organization={Mountain View, CA}
}

@inproceedings{john1995estimating,
	title={Estimating continuous distributions in Bayesian classifiers},
	author={John, George H and Langley, Pat},
	booktitle={Proceedings of the Eleventh conference on Uncertainty in artificial intelligence},
	pages={338--345},
	year={1995},
	organization={Morgan Kaufmann Publishers Inc.}
}

@inproceedings{freund1999alternating,
	title={The alternating decision tree learning algorithm},
	author={Freund, Yoav and Mason, Llew},
	booktitle={icml},
	volume={99},
	pages={124--133},
	year={1999}
}

@article{bhargava2013decision,
	title={Decision tree analysis on j48 algorithm for data mining},
	author={Bhargava, Neeraj and Sharma, Girja and Bhargava, Ritu and Mathuria, Manish},
	journal={Proceedings of International Journal of Advanced Research in Computer Science and Software Engineering},
	volume={3},
	number={6},
	year={2013}
}

@book{breiman2017classification,
	title={Classification and regression trees},
	author={Breiman, Leo},
	year={2017},
	publisher={Routledge}
}

@book{friedman2001elements,
	title={The elements of statistical learning},
	author={Friedman, Jerome and Hastie, Trevor and Tibshirani, Robert},
	volume={1},
	number={10},
	year={2001},
	publisher={Springer series in statistics New York}
}

@article{chang2011libsvm,
	title={LIBSVM: A library for support vector machines},
	author={Chang, Chih-Chung and Lin, Chih-Jen},
	journal={ACM transactions on intelligent systems and technology (TIST)},
	volume={2},
	number={3},
	pages={27},
	year={2011},
	publisher={Acm}
}

@book{scholkopf2001learning,
	title={Learning with kernels: support vector machines, regularization, optimization, and beyond},
	author={Scholkopf, Bernhard and Smola, Alexander J},
	year={2001},
	publisher={MIT press}
}

@article{amari1999improving,
	title={Improving support vector machine classifiers by modifying kernel functions},
	author={Amari, Shun-ichi and Wu, Si},
	journal={Neural Networks},
	volume={12},
	number={6},
	pages={783--789},
	year={1999},
	publisher={Elsevier}
}

@article{wu2004probability,
	title={Probability estimates for multi-class classification by pairwise coupling},
	author={Wu, Ting-Fan and Lin, Chih-Jen and Weng, Ruby C},
	journal={Journal of Machine Learning Research},
	volume={5},
	number={Aug},
	pages={975--1005},
	year={2004}
}

@inproceedings{guyon1993automatic,
	title={Automatic capacity tuning of very large VC-dimension classifiers},
	author={Guyon, Isabelle and Boser, B and Vapnik, Vladimir},
	booktitle={Advances in neural information processing systems},
	pages={147--155},
	year={1993}
}

@article{smola2004tutorial,
	title={A tutorial on support vector regression},
	author={Smola, Alex J and Sch{\"o}lkopf, Bernhard},
	journal={Statistics and computing},
	volume={14},
	number={3},
	pages={199--222},
	year={2004},
	publisher={Springer}
}

@inproceedings{dietterich2000ensemble,
	title={Ensemble methods in machine learning},
	author={Dietterich, Thomas G},
	booktitle={International workshop on multiple classifier systems},
	pages={1--15},
	year={2000},
	organization={Springer}
}

@article{kotsiantis2007supervised,
	title={Supervised machine learning: A review of classification techniques},
	author={Kotsiantis, Sotiris B and Zaharakis, I and Pintelas, P},
	journal={Emerging artificial intelligence applications in computer engineering},
	volume={160},
	pages={3--24},
	year={2007}
}

@article{breiman1999pasting,
	title={Pasting small votes for classification in large databases and on-line},
	author={Breiman, Leo},
	journal={Machine learning},
	volume={36},
	number={1-2},
	pages={85--103},
	year={1999},
	publisher={Springer}
}

@article{breiman1996bagging,
	title={Bagging predictors},
	author={Breiman, Leo},
	journal={Machine learning},
	volume={24},
	number={2},
	pages={123--140},
	year={1996},
	publisher={Springer}
}

@article{barandiaran1998random,
	title={The random subspace method for constructing decision forests},
	author={Barandiaran, I{\~n}igo},
	journal={IEEE transactions on pattern analysis and machine intelligence},
	volume={20},
	number={8},
	year={1998}
}

@InProceedings{ref10.1007/978-3-642-33460-3_28,
	author={Louppe, Gilles
	and Geurts, Pierre},
	editor={Flach, Peter A.
	and De Bie, Tijl
	and Cristianini, Nello},
	title={Ensembles on Random Patches},
	booktitle={Machine Learning and Knowledge Discovery in Databases},
	year={2012},
	publisher={Springer Berlin Heidelberg},
	address={Berlin, Heidelberg},
	pages={346-361},
	abstract={In this paper, we consider supervised learning under the assumption that the available memory is small compared to the dataset size. This general framework is relevant in the context of big data, distributed databases and embedded systems. We investigate a very simple, yet effective, ensemble framework that builds each individual model of the ensemble from a random patch of data obtained by drawing random subsets of both instances and features from the whole dataset. We carry out an extensive and systematic evaluation of this method on 29 datasets, using decision tree-based estimators. With respect to popular ensemble methods, these experiments show that the proposed method provides on par performance in terms of accuracy while simultaneously lowering the memory needs, and attains significantly better performance when memory is severely constrained.},
	isbn={978-3-642-33460-3}
}

@article{breiman1998,
	author = "Breiman, Leo",
	doi = "10.1214/aos/1024691079",
	fjournal = "The Annals of Statistics",
	journal = "Ann. Statist.",
	month = "06",
	number = "3",
	pages = "801--849",
	publisher = "The Institute of Mathematical Statistics",
	title = "Arcing classifier (with discussion and a rejoinder by the 		 author)",
	url = "https://doi.org/10.1214/aos/1024691079",
	volume = "26",
	year = "1998"
}

@Article{Breiman2001,
	author={Breiman, Leo},
	title={Random Forests},
	journal={Machine Learning},
	year={2001},
	volume={45},
	number={1},
	pages={5-32},
	abstract={Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund \& R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ***, 148-156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.},
	issn={1573-0565},
	doi={10.1023/A:1010933404324},
	url={https://doi.org/10.1023/A:1010933404324}
}

@incollection{abola1984protein,
	title={The protein data bank},
	author={Abola, Enrique E and Bernstein, Frances C and Koetzle, Thomas F},
	booktitle={Neutrons in Biology},
	pages={441--441},
	year={1984},
	publisher={Springer}
}

@book{vogelstein2002genetic,
	title={The genetic basis of human cancer},
	author={Vogelstein, Bert and Kinzler, Kenneth W and others},
	volume={821},
	year={2002},
	publisher={McGraw-Hill New York}
}

@Article{Kluijtmans1996,
	author={Kluijtmans, L. A.
	and van den Heuvel, L. P.
	and Boers, G. H.
	and Frosst, P.
	and Stevens, E. M.
	and van Oost, B. A.
	and den Heijer, M.
	and Trijbels, F. J.
	and Rozen, R.
	and Blom, H. J.},
	title={Molecular genetic analysis in mild hyperhomocysteinemia: a common mutation in the methylenetetrahydrofolate reductase gene is a genetic risk factor for cardiovascular disease},
	journal={Am J Hum Genet},
	year={1996},
	month={Jan},
	volume={58},
	number={1},
	pages={35-41},
	abstract={Mild hyperhomocysteinemia is an established risk factor for cardiovascular disease. Genetic aberrations in the cystathionine beta-synthase (CBS) and methylenetetrahydrofolate reductase (MTHFR) genes may account for reduced enzyme activities and elevated plasma homocysteine levels. In 15 unrelated Dutch patients with homozygous CBS deficiency, we observed the 833T-->C (I278T) mutation in 50\% of the alleles. Very recently, we identified a common mutation (677C-->T; A-->V) in the MTHFR gene, which, in homozygous state, is responsible for the thermolabile phenotype and which is associated with decreased specific MTHRF activity and elevated homocysteine levels. We screened 60 cardiovascular patients and 111 controls for these two mutations, to determine whether these mutations are risk factors for premature cardiovascular disease. Heterozygosity for the 833T-->C mutation in the CBS gene was observed in one individual of the control group but was absent in patients with premature cardiovascular disease. Homozygosity for the 677C-->T mutation in the MTHFR gene was found in (15\%) of 60 cardiovascular patients and in only 6 (approximately 5\%) of 111 control individuals (odds ratio 3.1 [95\% confidence interval 1.0-9.2]). Because of both the high prevalence of the 833T-->C mutation among homozygotes for CBS deficiency and its absence in 60 cardiovascular patients, we may conclude that heterozygosity for CBS deficiency does not appear to be involved in premature cardiovascular disease. However, a frequent homozygous mutation in the MTHFR gene is associated with a threefold increase in risk for premature cardiovascular disease.},
	note={8554066[pmid]},
	note={PMC1914961[pmcid]},
	issn={0002-9297},
	url={https://www.ncbi.nlm.nih.gov/pubmed/8554066}
}

@article{jamaVHL,

author = {Linehan, W. Marston and Lerman, Michael I. and Zbar, Berton},

title = "{Identification of the von Hippel-Lindau (VHL) Gene: Its Role in Renal Cancer}",

journal = {JAMA},

volume = {273},

number = {7},

pages = {564-570},

year = {1995},

month = {02},

abstract = "{RENAL carcinoma, the most common cancer of the kidney, occurs in over 27000 individuals in the United States each year and is responsible for over 11000 deaths annually.1 The incidence of renal carcinoma worldwide has been increasing at an annual rate of approximately 2\\%.1 Although renal carcinoma has been reported in children as young as 3 years, it most commonly occurs in adults between the ages of 50 and 70 years.2 Renal carcinoma affects males twice as frequently as females and accounts for approximately 3\\% of adult cancers. Although a number of hormonal, environmental, cellular, and genetic factors have been evaluated, little is known about the etiology of renal cancer.3 There is an increased incidence of renal cancer among workers exposed to asbestos and among leather workers,4,5 and a strong correlation has been found with cigarette smoking.6,7 In patients with end-stage renal disease, particularly}",

issn = {0098-7484},

doi = {10.1001/jama.1995.03520310062031},

url = {https://doi.org/10.1001/jama.1995.03520310062031},

eprint = {https://jamanetwork.com/journals/jama/articlepdf/387014/jama\_273\_7\_031.pdf},

}

@incollection{CLIFFORD200185,
	title = "von Hippel-Lindau disease: Clinical and molecular perspectives",
	series = "Advances in Cancer Research",
	publisher = "Academic Press",
	volume = "82",
	pages = "85 - 105",
	year = "2001",
	issn = "0065-230X",
	doi = "https://doi.org/10.1016/S0065-230X(01)82003-0",
	url = "http://www.sciencedirect.com/science/article/pii/S0065230X01820030",
	author = "Steven C. Clifford and Eamonn R. Maher",
	abstract = "Von Hippel-Lindau (VHL) disease (MIM 193300) is the most common cause of familial clear cell renal cell carcinoma (RCC). VHL disease results from germline mutations in the VHL tumor suppressor gene and is characterized by variable expression and the development of benign and malignant neoplasms in multiple organs. The clinical management of VHL disease is challenging and requires a coordinated multidisciplinary approach. However, early detection of VHL tumors by annual surveillance has improved the prognosis for VHL gene carriers. Complex genotype-phenotype correlations for the major manifestations of VHL disease result from allelic heterogeneity and suggest that the VHL gene product has multiple and tissue-specific functions. Recent studies suggest that the VHL protein represents the adaptor unit of an Skp1-Cdc53/Cul1-F-box (SCF)-like protein complex which targets specific proteins for ubiquitinylation and proteolysis. Tumors from VHL patients and sporadic tumors with VHL gene inactivation (e.g., most clear cell RCC) are hypervascular and overexpress hypoxia-inducible mRNAs such as vascular epithelial growth factor (VEGF). Recently, pVHL has been shown to regulate proteolysis of the transcription factors HIF-1 and HIF-2 (EPAS). Thus absence or inactivation of pVHL leads to constitutive HIF-1 and HIF-2 expression, which activates transcription of VEGF and other hypozia-inducible mRNAs. Evidence for further pVHL functions including roles in fibronectin metabolism and cell cycle regulation has also been reported, but it is unclear whether these functions are mediated via pVHL-targeted proteolysis or other mechanisms. Clinical and laboratory studies of VHL disease have provided a paradigm for demonstrating the importance of familial cancer syndromes in elucidating mechanisms of tumorigenesis in familial and sporadic cancer."
}

@article {Maher443,
	author = {Maher, E R and Iselius, L and Yates, J R and Littler, M and Benjamin, C and Harris, R and Sampson, J and Williams, A and Ferguson-Smith, M A and Morton, N},
	title = {Von Hippel-Lindau disease: a genetic study.},
	volume = {28},
	number = {7},
	pages = {443--447},
	year = {1991},
	doi = {10.1136/jmg.28.7.443},
	publisher = {BMJ Publishing Group Ltd},
	abstract = {Genetic aspects of von Hippel-Lindau (VHL) disease were studied in familial and isolated cases. Complex segregation analysis with pointers was performed in 38 kindreds with two or more affected members. Dominant inheritance with almost complete penetrance in the highest age classes (0.96 at 51 to 60 and 0.99 at 61 to 70 years) was confirmed and there was no evidence of heterogeneity between families ascertained through complete and incomplete selection. The point prevalence of heterozygotes in East Anglia was 1.89/100,000 (1/53,000) persons with an estimated birth incidence of 2.73/100,000 (1/36,000) live births. Reproductive fitness was 0.83. Direct and indirect estimates of the mutation rate were 4.4 (95\% CI 0.9 to 7.9) x 10(-6)/gene/generation and 2.32 x 10(-6)/gene/generation respectively. There was no significant association between parental age or birth order and new mutations for VHL disease.},
	issn = {0022-2593},
	URL = {https://jmg.bmj.com/content/28/7/443},
	eprint = {https://jmg.bmj.com/content/28/7/443.full.pdf},
	journal = {Journal of Medical Genetics}
}

@Article{KaelinJr2002,
	author={Kaelin Jr, William G.},
	title={Molecular basis of the VHL hereditary cancer syndrome},
	journal={Nature Reviews Cancer},
	year={2002},
	month={Sep},
	day={01},
	publisher={Nature Publishing Group SN  -},
	volume={2},
	pages={673 EP  -},
	note={Review Article},
	url={https://doi.org/10.1038/nrc885}
}

@article{LONSER20032059,
	title = "von Hippel-Lindau disease",
	journal = "The Lancet",
	volume = "361",
	number = "9374",
	pages = "2059 - 2067",
	year = "2003",
	issn = "0140-6736",
	doi = "https://doi.org/10.1016/S0140-6736(03)13643-4",
	url = "http://www.sciencedirect.com/science/article/pii/S0140673603136434",
	author = "Russell R Lonser and Gladys M Glenn and McClellan Walther and Emily Y Chew and Steven K Libutti and W Marston Linehan and Edward H Oldfield",
	abstract = "Summary
	von Hippel-Lindau disease is a heritable multisystem cancer syndrome that is associated with a germline mutation of the VHL tumour suppressor gene on the short arm of chromosome 3. This disorder is not rare (about one in 36 000 livebirths) and is inherited as a highly penetrant autosomal dominant trait (ie, with a high individual risk of disease). Affected individuals are at risk of developing various benign and malignant tumours of the central nervous system, kidneys, adrenal glands, pancreas, and reproductive adnexal organs. Because of the complexities associated with management of the various types of tumours in this disease, treatment is multidisciplinary. We present an overview of the clinical aspects, management, and treatment options for von Hippel-Lindau disease."
}

@article{duan1995inhibition,
	title={Inhibition of transcription elongation by the VHL tumor suppressor protein},
	author={Duan, D Roxanne and Pause, Arnim and Burgess, Wilson H and Aso, Teijiro and Chen, DY and Garrett, Karla P and Conaway, Ronald C and Conaway, Joan W and Linehan, W Marston and Klausner, Richard D},
	journal={Science},
	volume={269},
	number={5229},
	pages={1402--1406},
	year={1995},
	publisher={American Association for the Advancement of Science}
}

@article{softwareVHL,

author = {Gallou, Catherine and Junien, Claudine and Joly, Dominique and Staroz, Frédéric and Orfanelli, Marie Thérèse and Béroud, Christophe},

title = "{Software and database for the analysis of mutations in the VHL gene}",

journal = {Nucleic Acids Research},

volume = {26},

number = {1},

pages = {256-258},

year = {1998},

month = {01},

abstract = "{ VHL is a tumor suppressor gene localized on chromosome 3p25–26. Mutations of the VHL gene were described at first in the heritable von Hippel-Lindau disease and in the sporadic Renal Cell Carcinoma (RCC). More recently, VHL has also been shown to harbor mutations in mesothelioma and small cell lung carcinoma. To date more than 500 mutations have been identified. These mutations are mainly private with only one hot spot at codon 167 associated with pheochromocytoma. The germline mutations are essentially missense while somatic mutations include deletions, insertions and nonsense. To standardize the collection of these informations, facilitate the mutational analysis of the VHL gene and promote the genotype-phenotype analysis, a software package along with a computerized database have been created. The current database and the analysis software are accessible via the internet and world wide web interface at the URL: http://www.umd.necker.fr}",

issn = {0305-1048},

doi = {10.1093/nar/26.1.256},

url = {https://doi.org/10.1093/nar/26.1.256},

eprint = {http://oup.prod.sis.lan/nar/article-pdf/26/1/256/7046580/26-1-256.pdf},

}

@article {Kishida4544,
	author = {Kishida, Takeshi and Stackhouse, Thomas M. and Chen, Fan and Lerman, Michael I. and Zbar, Berton},
	title = {Cellular Proteins That Bind the von Hippel-Lindau Disease Gene Product: Mapping of Binding Domains and the Effect of Missense Mutations},
	volume = {55},
	number = {20},
	pages = {4544--4548},
	year = {1995},
	publisher = {American Association for Cancer Research},
	abstract = {The von Hippel-Lindau disease (VHL) gene is a novel tumor suppressor gene that plays a role in the pathogenesis of renal cell carcinomas and hemangioblastomas of the central nervous system. To begin an evaluation of the biological functions of the VHL gene product (pVHL), we prepared bacterial fusion protein between glutathione S-transferase and wild-type or mutant pVHLs. The fusion proteins were used to identify cellular proteins that bind to pVHL in vitro. Monkey kidney cells transfected with wild-type or mutant VHL cDNAs were used to identify cellular proteins that bind to pVHL in vivo. Wild-type pVHL consistently bound two cellular proteins with apparent molecular masses of 10 and 14 kilodaltons that were designated p10 and p14, respectively. Mapping studies with a panel of VHL deletion mutant proteins demonstrated that p10 and p14 bound to a 32-amino acid peptide located in the carboxy terminal portion of pVHL. Missense mutations located within this 32-amino acid peptide abrogated the ability of the VHL protein to bind p10 and p14. Of 67 VHL families with identified germline mutations, 42 families had mutations predicted to affect the p10/p14-binding region. Maintenance of the integrity of the p10/p14-binding region appears to be essential for cellular growth regulation by pVHL. {\textcopyright}1995 American Association for Cancer Research.},
	issn = {0008-5472},
	URL = {http://cancerres.aacrjournals.org/content/55/20/4544},
	eprint = {http://cancerres.aacrjournals.org/content/55/20/4544.full.pdf},
	journal = {Cancer Research}
}

@article {Schoenfeld8507,
	author = {Schoenfeld, Alan R. and Davidowitz, Eliot J. and Burk, Robert D.},
	title = {Elongin BC complex prevents degradation of von Hippel-Lindau tumor suppressor gene products},
	volume = {97},
	number = {15},
	pages = {8507--8512},
	year = {2000},
	doi = {10.1073/pnas.97.15.8507},
	publisher = {National Academy of Sciences},
	abstract = {Inactivation of the von Hippel-Lindau (VHL) tumor suppressor gene causes the familial cancer syndrome, VHL disease, characterized by a predisposition to renal cell carcinoma and other tumor types. Loss of VHL gene function also is found in a majority of sporadic renal carcinomas. A preponderance of the tumor-disposing inherited missense mutations detected in VHL disease are within the elongin-binding domain of VHL. This region mediates the formation of a multiprotein VHL complex containing elongin B, elongin C, cul-2, and Rbx1. This VHL complex is thought to function as an E3 ubiquitin ligase. Here, we report that VHL proteins harboring mutations which disrupt elongin binding are unstable and rapidly degraded by the proteasome. In contrast, wild-type VHL proteins are directly stabilized by associating with both elongins B and C. In addition, elongins B and C are stabilized through their interactions with each other and VHL. Thus, the entire VHL/elongin complex is resistant to proteasomal degradation. Because the elongin-binding domain of VHL is frequently mutated in cancers, these results suggest that loss of elongin binding causes tumorigenesis by compromising VHL protein stability and/or potential VHL ubiquitination functions. VHL,von Hippel-Lindau;pVHL,von Hippel-Lindau tumor suppressor gene products;MPR,Met-Pro-Arg;SOCS,suppressor of cytokine signaling;WT,wild-type;R167W,Arg-167 to Trp;RC161/2QW,Arg-161 to Gln/Cys-162 to Trp;Y98H,Tyr-98 to His},
	issn = {0027-8424},
	URL = {https://www.pnas.org/content/97/15/8507},
	eprint = {https://www.pnas.org/content/97/15/8507.full.pdf},
	journal = {Proceedings of the National Academy of Sciences}
}

@article {vhlData,

Title = {von Hippel-Lindau disease affecting 43 members of a single kindred},

Author = {Lamiell, JM and Salazar, FG and Hsia, YE},

Number = {1},

Volume = {68},

Month = {January},

Year = {1989},

Journal = {Medicine},

ISSN = {0025-7974},

Pages = {1—29},

URL = {http://europepmc.org/abstract/MED/2642584}

}

@article{phillips1994neurological,
	title={Neurological abnormalities associated with feline immunodeficiency virus infection},
	author={Phillips, TR and Prospero-Garcia, O and Puaoi, DL and Lerner, DL and Fox, Howard S and Olmsted, RA and Bloom, FE and Henriksen, SJ and Elder, JH},
	journal={Journal of General Virology},
	volume={75},
	number={5},
	pages={979--987},
	year={1994},
	publisher={Microbiology Society}
}

@article{CAO2013745,
	title = "Advance and Prospects of AdaBoost Algorithm",
	journal = "Acta Automatica Sinica",
	volume = "39",
	number = "6",
	pages = "745 - 758",
	year = "2013",
	issn = "1874-1029",
	doi = "https://doi.org/10.1016/S1874-1029(13)60052-X",
	url = "http://www.sciencedirect.com/science/article/pii/S187410291360052X",
	author = "Ying CAO and Qi-Guang MIAO and Jia-Chen LIU and Lin GAO",
	keywords = "Ensemble learning, Boosting, AdaBoost, generalization error, classification margin, multiclass classification",
	abstract = "AdaBoost is one of the most excellent Boosting algorithms. It has a solid theoretical basis and has made great success in practical applications. AdaBoost can boost a weak learning algorithm with an accuracy slightly better than random guessing into an arbitrarily accurate strong learning algorithm, bringing about a new method and a new design idea to the design of learning algorithm. This paper first introduces how Boosting, just a conjecture when proposed, was proved right, and how this proof led to the origin of AdaBoost algorithm. Second, training and generalization error of AdaBoost are analyzed to explain why AdaBoost can successfully improve the accuracy of a weak learning algorithm. Third, different theoretical models to analyze AdaBoost are given. Meanwhile, many variants derived from these models are presented. Fourth, extensions of binary-class AdaBoost to multiclass AdaBoost are described. Besides, applications of AdaBoost algorithm are also introduced. Finally, interested directions which need to be further studied are discussed. For Boosting theory, these directions include deducing a tighter generalization error bound and finding a more precise weak learning condition in multiclass problem. For AdaBoost, the stopping conditions, the way to enhance anti-noise capability and how to improve the accuracy by optimizing the diversity of the base classifiers, are good questions to be in-depth researched."
}

@article{hastie2009multi,
	title={Multi-class adaboost},
	author={Hastie, Trevor and Rosset, Saharon and Zhu, Ji and Zou, Hui},
	journal={Statistics and its Interface},
	volume={2},
	number={3},
	pages={349--360},
	year={2009},
	publisher={International Press of Boston}
}

@article{FRIEDMAN2002367,
	title = "Stochastic gradient boosting",
	journal = "Computational Statistics AND Data Analysis",
	volume = "38",
	number = "4",
	pages = "367 - 378",
	year = "2002",
	note = "Nonlinear Methods and Data Mining",
	issn = "0167-9473",
	doi = "https://doi.org/10.1016/S0167-9473(01)00065-2",
	url = "http://www.sciencedirect.com/science/article/pii/S0167947301000652",
	author = "Jerome H. Friedman",
	abstract = "Gradient boosting constructs additive regression models by sequentially fitting a simple parameterized function (base learner) to current “pseudo”-residuals by least squares at each iteration. The pseudo-residuals are the gradient of the loss functional being minimized, with respect to the model values at each training data point evaluated at the current step. It is shown that both the approximation accuracy and execution speed of gradient boosting can be substantially improved by incorporating randomization into the procedure. Specifically, at each iteration a subsample of the training data is drawn at random (without replacement) from the full training data set. This randomly selected subsample is then used in place of the full sample to fit the base learner and compute the model update for the current iteration. This randomized approach also increases robustness against overcapacity of the base learner."
}

@article{gradient,
	ISSN = {00905364},
	URL = {http://www.jstor.org/stable/2699986},
	abstract = {Function estimation/approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest-descent minimization. A general gradient descent "boosting" paradigm is developed for additive expansions based on any fitting criterion. Specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification. Special enhancements are derived for the particular case where the individual additive components are regression trees, and tools for interpreting such "TreeBoost" models are presented. Gradient boosting of regression trees produces competitive, highly robust, interpretable procedures for both regression and classification, especially appropriate for mining less than clean data. Connections between this approach and the boosting methods of Freund and Shapire and Friedman, Hastie and Tibshirani are discussed.},
	author = {Jerome H. Friedman},
	journal = {The Annals of Statistics},
	number = {5},
	pages = {1189--1232},
	publisher = {Institute of Mathematical Statistics},
	title = {Greedy Function Approximation: A Gradient Boosting Machine},
	volume = {29},
	year = {2001}
}

@article{bengio2013representation,
	title={Representation learning: A review and new perspectives},
	author={Bengio, Yoshua and Courville, Aaron and Vincent, Pascal},
	journal={IEEE transactions on pattern analysis and machine intelligence},
	volume={35},
	number={8},
	pages={1798--1828},
	year={2013},
	publisher={IEEE}
}

@article{MAL-006,

url = {http://dx.doi.org/10.1561/2200000006},

year = {2009},

volume = {2},

journal = {Foundations and Trends® in Machine Learning},

title = {Learning Deep Architectures for AI},

doi = {10.1561/2200000006},

issn = {1935-8237},

number = {1},

pages = {1-127},

author = {Yoshua Bengio}

}

@article{SIG-039,

url = {http://dx.doi.org/10.1561/2000000039},

year = {2014},

volume = {7},

journal = {Foundations and Trends® in Signal Processing},

title = {Deep Learning: Methods and Applications},

doi = {10.1561/2000000039},

issn = {1932-8346},

number = {3–4},

pages = {197-387},

author = {Li Deng and Dong Yu}

}

@article{arel2010deep,
	title={Deep machine learning-a new frontier in artificial intelligence research},
	author={Arel, Itamar and Rose, Derek C and Karnowski, Thomas P and others},
	journal={IEEE computational intelligence magazine},
	volume={5},
	number={4},
	pages={13--18},
	year={2010},
	publisher={The University of Tennessee}
}

@incollection{HECHTNIELSEN199265,
	title = "III.3 - Theory of the Backpropagation Neural Network**Based on “nonindent” by Robert Hecht-Nielsen, which appeared in Proceedings of the International Joint Conference on Neural Networks 1, 593–611, June 1989. © 1989 IEEE.",
	editor = "Harry Wechsler",
	booktitle = "Neural Networks for Perception",
	publisher = "Academic Press",
	pages = "65 - 93",
	year = "1992",
	isbn = "978-0-12-741252-8",
	doi = "https://doi.org/10.1016/B978-0-12-741252-8.50010-8",
	url = "http://www.sciencedirect.com/science/article/pii/B9780127412528500108",
	author = "ROBERT HECHT-NIELSEN",
	abstract = "Publisher Summary
	This chapter presents a survey of the elementary theory of the basic backpropagation neural network architecture, covering the areas of architectural design, performance measurement, function approximation capability, and learning. The survey includes a formulation of the backpropagation neural network architecture to make it a valid neural network and a proof that the backpropagation mean squared error function exists and is differentiable. Also included in the survey is a theorem showing that any L2 function can be implemented to any desired degree of accuracy with a three-layer backpropagation neural network. An appendix presents a speculative neurophysiological model illustrating the way in which the backpropagation neural network architecture might plausibly be implemented in the mammalian brain for corticocortical learning between nearby regions of cerebral cortex. One of the crucial decisions in the design of the backpropagation architecture is the selection of a sigmoidal activation function."
}

@ARTICLE{80266,
	author={D. W. {Ruck} and S. K. {Rogers} and M. {Kabrisky} and M. E. {Oxley} and B. W. {Suter}},
	journal={IEEE Transactions on Neural Networks},
	title={The multilayer perceptron as an approximation to a Bayes optimal discriminant function},
	year={1990},
	volume={1},
	number={4},
	pages={296-298},
	keywords={neural nets;probability;multiple class problems;neural networks;multilayer perceptron;Bayes optimal discriminant function;classifier;backpropagation;two-class problem;probability;unit activation function;Multilayer perceptrons;Probability density function;Backpropagation;Pattern recognition;Bayesian methods;Books;Image analysis;Multi-layer neural network;Neural networks},
	doi={10.1109/72.80266},
	ISSN={1045-9227},
	month={Dec},}

@article{crossval,
	author = { Gene H.   Golub  and  Michael   Heath  and  Grace   Wahba },
	title = {Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter},
	journal = {Technometrics},
	volume = {21},
	number = {2},
	pages = {215-223},
	year  = {1979},
	publisher = {Taylor & Francis},
	doi = {10.1080/00401706.1979.10489751},

	URL = {
	https://amstat.tandfonline.com/doi/abs/10.1080/00401706.1979.10489751

	},
	eprint = {
	https://amstat.tandfonline.com/doi/pdf/10.1080/00401706.1979.10489751

	}

}

@inproceedings{kohavi1995study,
	title={A study of cross-validation and bootstrap for accuracy estimation and model selection},
	author={Kohavi, Ron and others},
	booktitle={Ijcai},
	volume={14},
	number={2},
	pages={1137--1145},
	year={1995},
	organization={Montreal, Canada}
}

@inproceedings{indyk1998approximate,
	title={Approximate nearest neighbors: towards removing the curse of dimensionality},
	author={Indyk, Piotr and Motwani, Rajeev},
	booktitle={Proceedings of the thirtieth annual ACM symposium on Theory of computing},
	pages={604--613},
	year={1998},
	organization={ACM}
}

@techreport{sarwar2000application,
	title={Application of dimensionality reduction in recommender system-a case study},
	author={Sarwar, Badrul and Karypis, George and Konstan, Joseph and Riedl, John},
	year={2000},
	institution={Minnesota Univ Minneapolis Dept of Computer Science}
}

@article{van2009dimensionality,
	title={Dimensionality reduction: a comparative},
	author={Van Der Maaten, Laurens and Postma, Eric and Van den Herik, Jaap},
	journal={J Mach Learn Res},
	volume={10},
	number={66-71},
	pages={13},
	year={2009}
}

@article{hawkins2004problem,
	title={The problem of overfitting},
	author={Hawkins, Douglas M},
	journal={Journal of chemical information and computer sciences},
	volume={44},
	number={1},
	pages={1--12},
	year={2004},
	publisher={ACS Publications}
}

@article{japkowicz2002class,
	title={The class imbalance problem: A systematic study},
	author={Japkowicz, Nathalie and Stephen, Shaju},
	journal={Intelligent data analysis},
	volume={6},
	number={5},
	pages={429--449},
	year={2002},
	publisher={IOS Press}
}

@article{ancien2018prediction,
	title={Prediction and interpretation of deleterious coding variants in terms of protein structural stability},
	author={Ancien, Fran{\c{c}}ois and Pucci, Fabrizio and Godfroid, Maxime and Rooman, Marianne},
	journal={Scientific reports},
	volume={8},
	number={1},
	pages={4480},
	year={2018},
	publisher={Nature Publishing Group}
}

@article{broom2017computational,
	title={Computational tools help improve protein stability but with a solubility tradeoff},
	author={Broom, Aron and Jacobi, Zachary and Trainor, Kyle and Meiering, Elizabeth M},
	journal={Journal of Biological Chemistry},
	volume={292},
	number={35},
	pages={14349--14361},
	year={2017},
	publisher={ASBMB}
}

@article{capriotti2008three,
	title={A three-state prediction of single point mutations on protein stability changes},
	author={Capriotti, Emidio and Fariselli, Piero and Rossi, Ivan and Casadio, Rita},
	journal={BMC bioinformatics},
	volume={9},
	number={2},
	pages={S6},
	year={2008},
	publisher={BioMed Central}
}

@article{quan2016strum,
	title={STRUM: structure-based prediction of protein stability changes upon single-point mutation},
	author={Quan, Lijun and Lv, Qiang and Zhang, Yang},
	journal={Bioinformatics},
	volume={32},
	number={19},
	pages={2936--2946},
	year={2016},
	publisher={Oxford University Press}
}

@article{1gzp030,

author = {Potapov, Vladimir and Cohen, Mati and Schreiber, Gideon},

title = "{Assessing computational methods for predicting protein stability upon mutation: good on average but not in the details}",

journal = {Protein Engineering, Design and Selection},

volume = {22},

number = {9},

pages = {553-560},

year = {2009},

month = {06},

abstract = "{Methods for protein modeling and design advanced rapidly in recent years. At the heart of these computational methods is an energy function that calculates the free energy of the system. Many of these functions were also developed to estimate the consequence of mutation on protein stability or binding affinity. In the current study, we chose six different methods that were previously reported as being able to predict the change in protein stability (ΔΔG) upon mutation: CC/PBSA, EGAD, FoldX, I-Mutant2.0, Rosetta and Hunter. We evaluated their performance on a large set of 2156 single mutations, avoiding for each program the mutations used for training. The correlation coefficients between experimental and predicted ΔΔG values were in the range of 0.59 for the best and 0.26 for the worst performing method. All the tested computational methods showed a correct trend in their predictions, but failed in providing the precise values. This is not due to lack in precision of the experimental data, which showed a correlation coefficient of 0.86 between different measurements. Combining the methods did not significantly improve prediction accuracy compared to a single method. These results suggest that there is still room for improvement, which is crucial if we want forcefields to perform better in their various tasks.}",

issn = {1741-0126},

doi = {10.1093/protein/gzp030},

url = {https://doi.org/10.1093/protein/gzp030},

eprint = {http://oup.prod.sis.lan/peds/article-pdf/22/9/553/4354997/gzp030.pdf},

}

@article{masso2008accurate,
	title={Accurate prediction of stability changes in protein mutants by combining machine learning with structure based computational mutagenesis},
	author={Masso, Majid and Vaisman, Iosif I},
	journal={Bioinformatics},
	volume={24},
	number={18},
	pages={2002--2009},
	year={2008},
	publisher={Oxford University Press}
}

@article{getov2016saafec,
	title={SAAFEC: predicting the effect of single point mutations on protein folding free energy using a knowledge-modified MM/PBSA approach},
	author={Getov, Ivan and Petukh, Marharyta and Alexov, Emil},
	journal={International journal of molecular sciences},
	volume={17},
	number={4},
	pages={512},
	year={2016},
	publisher={Multidisciplinary Digital Publishing Institute}
}

@article{prot20185,
	title = {Large-scale prediction of protein geometry and stability changes for arbitrary single point mutations},
	author = {Bordner, A. J. and Abagyan, R. A.},
	journal = {Proteins: Structure, Function, and Bioinformatics},
	volume = {57},
	number = {2},
	pages = {400-413},
	keywords = {empirical potential, Monte Carlo optimization, denatured state, conformation, free energy},
	doi = {10.1002/prot.20185},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/prot.20185},
	eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/prot.20185},
	abstract = {Abstract We have developed a method to both predict the geometry and the relative stability of point mutants that may be used for arbitrary mutations. The geometry optimization procedure was first tested on a new benchmark of 2141 ordered pairs of X-ray crystal structures of proteins that differ by a single point mutation, the largest data set to date. An empirical energy function, which includes terms representing the energy contributions of the folded and denatured proteins and uses the predicted mutant side chain conformation, was fit to a training set consisting of half of a diverse set of 1816 experimental stability values for single point mutations in 81 different proteins. The data included a substantial number of small to large residue mutations not considered by previous prediction studies. After removing 22 (≈2\%) outliers, the stability calculation gave a standard deviation of 1.08 kcal/mol with a correlation coefficient of 0.82. The prediction method was then tested on the remaining half of the experimental data, giving a standard deviation of 1.10 kcal/mol and covariance of 0.66 for 97\% of the test set. A regression fit of the energy function to a subset of 137 mutants, for which both native and mutant structures were available, gave a prediction error comparable to that for the complete training set with predicted side chain conformations. We found that about half of the variation is due to conformation-independent residue contributions. Finally, a fit to the experimental stability data using these residue parameters exclusively suggests guidelines for improving protein stability in the absence of detailed structure information. Proteins 2004. © 2004 Wiley-Liss, Inc.},,
	year = {2004}
}

@Article{Wainreb2011,
	author={Wainreb, Gilad
	and Ashkenazy, Haim
	and Wolf, Lior
	and Ben-Tal, Nir
	and Dehouck, Yves},
	title={Protein stability: a single recorded mutation aids in predicting the effects of other mutations in the same amino acid site},
	journal={Bioinformatics},
	year={2011},
	month={Oct},
	day={13},
	volume={27},
	number={23},
	pages={3286-3292},
	abstract={Motivation: Accurate prediction of protein stability is important for understanding the molecular underpinnings of diseases and for the design of new proteins. We introduce a novel approach for the prediction of changes in protein stability that arise from a single-site amino acid substitution; the approach uses available data on mutations occurring in the same position and in other positions. Our algorithm, named Pro-Maya (Protein Mutant stAbilitY Analyzer), combines a collaborative filtering baseline model, Random Forests regression and a diverse set of features. Pro-Maya predicts the stability free energy difference of mutant versus wild type, denoted as DDG.Results: We evaluated our algorithm extensively using cross-validation on two previously utilized datasets of single amino acid mutations and a (third) validation set. The results indicate that using known DDG values of mutations at the query position improves the accuracy of DDG predictions for other mutations in that position. The accuracy of our predictions in such cases significantly surpasses that of similar methods, achieving, e.g. a Pearson's correlation coefficient of 0.79 and a root mean square error of 0.96 on the validation set. Because Pro-Maya uses a diverse set of features, including predictions using two other methods, it also performs slightly better than other methods in the absence of additional experimental data on the query positions.Availability: Pro-Maya is freely available via web server at http://bental.tau.ac.il/ProMaya.Contact:nirb@tauex.tau.ac.il; wolf@cs.tau.ac.ilSupplementary Information:Supplementary data are available at Bioinformatics online.},
	issn={1367-4803},
	doi={10.1093/bioinformatics/btr576},
	url={https://doi.org/10.1093/bioinformatics/btr576}
}

@Article{Sun2017,
	author={Sun, Lexuan
	and Li, Lin
	and Peng, Yunhui
	and Jia, Zhe
	and Alexov, Emil},
	title={Predicting protein-DNA binding free energy change upon missense mutations using modified MM/PBSA approach: SAMPDI webserver},
	journal={Bioinformatics},
	year={2017},
	month={Oct},
	day={28},
	volume={34},
	number={5},
	pages={779-786},
	abstract={Protein-DNA interactions are essential for regulating many cellular processes, such as transcription, replication, recombination and translation. Amino acid mutations occurring in DNA-binding proteins have profound effects on protein-DNA binding and are linked with many diseases. Hence, accurate and fast predictions of the effects of mutations on protein-DNA binding affinity are essential for understanding disease-causing mechanisms and guiding plausible treatments.Here we report a new method Single Amino acid Mutation binding free energy change of Protein-DNA Interaction (SAMPDI). The method utilizes modified Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) approach along with an additional set of knowledge-based terms delivered from investigations of the physicochemical properties of protein-DNA complexes. The method is benchmarked against experimentally determined binding free energy changes caused by 105 mutations in 13 proteins (compiled ProNIT database and data from recent references), and results in correlation coefficient of 0.72.http://compbio.clemson.edu/SAMPDISupplementary data are available at Bioinformatics online.},
	issn={1367-4803},
	doi={10.1093/bioinformatics/btx698},
	url={https://doi.org/10.1093/bioinformatics/btx698}
}

@article{petukh2016saambe,
	title={SAAMBE: webserver to predict the charge of binding free energy caused by amino acids mutations},
	author={Petukh, Marharyta and Dai, Luogeng and Alexov, Emil},
	journal={International journal of molecular sciences},
	volume={17},
	number={4},
	pages={547},
	year={2016},
	publisher={Multidisciplinary Digital Publishing Institute}
}

@Article{Alexov2012,
	author={Alexov, Emil
	and Zhang, Jie
	and Wang, Lin
	and Zhenirovskyy, Maxim
	and Gao, Yang
	and Zhang, Zhe},
	title={Predicting folding free energy changes upon single point mutations},
	journal={Bioinformatics},
	year={2012},
	month={Jan},
	day={11},
	volume={28},
	number={5},
	pages={664-671},
	abstract={Motivation: The folding free energy is an important characteristic of proteins stability and is directly related to protein's wild-type function. The changes of protein's stability due to naturally occurring mutations, missense mutations, are typically causing diseases. Single point mutations made in vitro are frequently used to assess the contribution of given amino acid to the stability of the protein. In both cases, it is desirable to predict the change of the folding free energy upon single point mutations in order to either provide insights of the molecular mechanism of the change or to design new experimental studies.Results: We report an approach that predicts the free energy change upon single point mutation by utilizing the 3D structure of the wild-type protein. It is based on variation of the molecular mechanics Generalized Born (MMGB) method, scaled with optimized parameters (sMMGB) and utilizing specific model of unfolded state. The corresponding mutations are built in silico and the predictions are tested against large dataset of 1109 mutations with experimentally measured changes of the folding free energy. Benchmarking resulted in root mean square deviation = 1.78 kcal/mol and slope of the linear regression fit between the experimental data and the calculations was 1.04. The sMMGB is compared with other leading methods of predicting folding free energy changes upon single mutations and results discussed with respect to various parameters.Availability: All the pdb files we used in this article can be downloaded from http://compbio.clemson.edu/downloadDir/mentaldisorders/sMMGB\_pdb.rarContact:ealexov@clemson.eduSupplementary information:Supplementary data are available at Bioinformatics online.},
	issn={1367-4803},
	doi={10.1093/bioinformatics/bts005},
	url={https://doi.org/10.1093/bioinformatics/bts005}
}

@Article{Forbes2010,
	author={Forbes, Simon A.
	and Bindal, Nidhi
	and Bamford, Sally
	and Cole, Charlotte
	and Kok, Chai Yin
	and Beare, David
	and Jia, Mingming
	and Shepherd, Rebecca
	and Leung, Kenric
	and Menzies, Andrew
	and Teague, Jon W.
	and Campbell, Peter J.
	and Stratton, Michael R.
	and Futreal, P. Andrew},
	title={COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer},
	journal={Nucleic Acids Research},
	year={2010},
	month={Oct},
	day={15},
	volume={39},
	number={suppl\_1},
	pages={D945-D950},
	abstract={COSMIC ( http://www.sanger.ac.uk/cosmic ) curates comprehensive information on somatic mutations in human cancer. Release v48 (July 2010) describes over 136 000 coding mutations in almost 542 000 tumour samples; of the 18 490 genes documented, 4803 (26\%) have one or more mutations. Full scientific literature curations are available on 83 major cancer genes and 49 fusion gene pairs (19 new cancer genes and 30 new fusion pairs this year) and this number is continually increasing. Key amongst these is TP53, now available through a collaboration with the IARC p53 database. In addition to data from the Cancer Genome Project (CGP) at the Sanger Institute, UK, and The Cancer Genome Atlas project (TCGA), large systematic screens are also now curated. Major website upgrades now make these data much more mineable, with many new selection filters and graphics. A Biomart is now available allowing more automated data mining and integration with other biological databases. Annotation of genomic features has become a significant focus; COSMIC has begun curating full-genome resequencing experiments, developing new web pages, export formats and graphics styles. With all genomic information recently updated to GRCh37, COSMIC integrates many diverse types of mutation information and is making much closer links with Ensembl and other data resources.},
	issn={0305-1048},
	doi={10.1093/nar/gkq929},
	url={https://doi.org/10.1093/nar/gkq929}
}

@Article{Tian2010,
	author="Tian, Jian
	and Wu, Ningfeng
	and Chu, Xiaoyu
	and Fan, Yunliu",
	title="Predicting changes in protein thermostability brought about by single- or multi-site mutations",
	journal="BMC Bioinformatics",
	year="2010",
	month="Jul",
	day="02",
	volume="11",
	number="1",
	pages="370",
	abstract="An important aspect of protein design is the ability to predict changes in protein thermostability arising from single- or multi-site mutations. Protein thermostability is reflected in the change in free energy ($\Delta$$\Delta$G) of thermal denaturation.",
	issn="1471-2105",
	doi="10.1186/1471-2105-11-370",
	url="https://doi.org/10.1186/1471-2105-11-370"
}

@Article{Barenboim2008,
	author={Barenboim, Maxim
	and Masso, Majid
	and Vaisman, Iosif I.
	and Jamison, D. Curtis},
	title={Statistical geometry based prediction of nonsynonymous SNP functional effects using random forest and neuro-fuzzy classifiers},
	journal={Proteins: Structure, Function, and Bioinformatics},
	year={2008},
	month={Jun},
	day={01},
	publisher={John Wiley \& Sons, Ltd},
	volume={71},
	number={4},
	pages={1930-1939},
	keywords={single nucleotide polymorphism},
	keywords={computational geometry},
	keywords={artificial intelligence},
	keywords={algorithms},
	keywords={protein conformation},
	keywords={statistical models},
	keywords={complex disease},
	keywords={tertiary protein structure},
	keywords={computational biology},
	keywords={amino acid substitution},
	abstract={Abstract There is substantial interest in methods designed to predict the effect of nonsynonymous single nucleotide polymorphisms (nsSNPs) on protein function, given their potential relationship to heritable diseases. Current state-of-the-art supervised machine learning algorithms, such as random forest (RF), train models that classify single amino acid mutations in proteins as either neutral or deleterious to function. However, it is frequently the case that the functional effect of a polymorphism on a protein resides between these two extremes. The utilization of classifiers that incorporate fuzzy logic provides a natural extension in order to account for the spectrum of possible functional consequences. We generated a dataset of single amino acid substitutions in human proteins having known three-dimensional structures. Each variant was uniquely represented as a feature vector that included computational geometry and knowledge-based statistical potential predictors obtained though application of Delaunay tessellation of protein structures. Additional attributes consisted of physicochemical properties of the native and replacement amino acids as well as topological location of the mutated residue position in the solved structure. Classification performance of the RF algorithm was evaluated on a training set consisting of the disease-associated and neutral nsSNPs taken from our dataset, and attributes were ranked according to their relative importance. Similarly, we evaluated the performance of adaptive neuro-fuzzy inference system (ANFIS). The utility of statistical geometry predictors was compared with that of traditional structural and evolutionary attributes employed by other researchers, revealing an equally effective yet complementary methodology. Among all attributes in our feature set, the statistical geometry predictors were found to be the most highly ranked. On the basis of the AUC (area under the ROC curve) measure of performance, the ANFIS and RF models were equally effective when only statistical geometry features were utilized. Tenfold cross-validation studies evaluating AUC, balanced error rate (BER), and Matthew's correlation coefficient (MCC) showed that our RF model was at least comparable with the well-established methods of SIFT and PolyPhen. The trained RF and ANFIS models were each subsequently used to predict the disease potential of human nsSNPs in our dataset that are currently unclassified (http://rna.gmu.edu/FuzzySnps/). Proteins 2008. ? 2008 Wiley-Liss, Inc.},
	issn={0887-3585},
	doi={10.1002/prot.21838},
	url={https://doi.org/10.1002/prot.21838}
}

@Article{Schymkowitz2005,
	author={Schymkowitz, Joost
	and Borg, Jesper
	and Stricher, Francois
	and Nys, Robby
	and Rousseau, Frederic
	and Serrano, Luis},
	title={The FoldX web server: an online force field},
	journal={Nucleic Acids Research},
	year={2005},
	month={Jul},
	day={01},
	volume={33},
	number={suppl\_2},
	pages={W382-W388},
	abstract={FoldX is an empirical force field that was developed for the rapid evaluation of the effect of mutations on the stability, folding and dynamics of proteins and nucleic acids. The core functionality of FoldX, namely the calculation of the free energy of a macromolecule based on its high-resolution 3D structure, is now publicly available through a web server at http://foldx.embl.de/ . The current release allows the calculation of the stability of a protein, calculation of the positions of the protons and the prediction of water bridges, prediction of metal binding sites and the analysis of the free energy of complex formation. Alanine scanning, the systematic truncation of side chains to alanine, is also included. In addition, some reporting functions have been added, and it is now possible to print both the atomic interaction networks that constitute the protein, print the structural and energetic details of the interactions per atom or per residue, as well as generate a general quality report of the pdb structure. This core functionality will be further extended as more FoldX applications are developed.},
	issn={0305-1048},
	doi={10.1093/nar/gki387},
	url={https://doi.org/10.1093/nar/gki387}
}

@article{petukhov1999local,
	title={Local water bridges and protein conformational stability},
	author={Petukhov, Michael and Cregut, David and Soares, Claudio M and Serrano, Luis},
	journal={Protein Science},
	volume={8},
	number={10},
	pages={1982--1989},
	year={1999},
	publisher={Cambridge University Press}
}

@article{munoz1996local,
	title={Local versus nonlocal interactions in protein folding and stability--an experimentalist's point of view},
	author={Mu{\~n}oz, Victor and Serrano, Luis},
	journal={Folding and Design},
	volume={1},
	number={4},
	pages={R71--R77},
	year={1996},
	publisher={Elsevier}
}

@article{munoz1995hydrophobic,
	title={The hydrophobic-staple motif and a role for loop-residues in $\alpha$-helix stability and protein folding},
	author={Mu{\~n}oz, Victor and Blanco, Francisco J and Serrano, Luis},
	journal={Nature structural biology},
	volume={2},
	number={5},
	pages={380},
	year={1995},
	publisher={Nature Publishing Group}
}

@article{abagyan1994biased,
	title={Biased probability Monte Carlo conformational searches and electrostatic calculations for peptides and proteins},
	author={Abagyan, Ruben and Totrov, Maxim},
	journal={Journal of molecular biology},
	volume={235},
	number={3},
	pages={983--1002},
	year={1994},
	publisher={Elsevier}
}

@article{vijayakumar1998electrostatic,
	title={Electrostatic enhancement of diffusion-controlled protein-protein association: comparison of theory and experiment on barnase and barstar},
	author={Vijayakumar, M and Wong, Kwan-Yin and Schreiber, Gideon and Fersht, Alan R and Szabo, Attila and Zhou, Huan-Xiang},
	journal={Journal of molecular biology},
	volume={278},
	number={5},
	pages={1015--1024},
	year={1998},
	publisher={Elsevier}
}

@Article{Sanchez2008,
	author={S{\'a}nchez, Ignacio E.
	and Beltrao, Pedro
	and Stricher, Francois
	and Schymkowitz, Joost
	and Ferkinghoff-Borg, Jesper
	and Rousseau, Frederic
	and Serrano, Luis},
	title={Genome-Wide Prediction of SH2 Domain Targets Using Structural Information and the FoldX Algorithm},
	journal={PLOS Computational Biology},
	year={2008},
	month={Apr},
	day={04},
	publisher={Public Library of Science},
	volume={4},
	number={4},
	pages={e1000052},
	abstract={Author SummaryUnderstanding the functional role of every protein in the cell is a long-standing goal of cellular biology. An important step in this direction is to discover how and when proteins interact inside the cell to accomplish their tasks. Many of the cellular functions depend on reversible protein modifications like phosphorylation. To sense these modifications, cells have protein domains capable of binding phosphorylated proteins such as the SH2 domain. In this work, we show that it is possible to use the three-dimensional structure of protein domains to predict its binding preferences. Using a computational tool called FoldX, we have predicted the binding specificity of several human SH2 domains. These predictions, based on the computational analysis of the 3-D structure, were shown to be of similar accuracy as those obtained from experimental binding assays. We show here that it is also possible to understand how a mutation changes the binding preference of protein binding domains, opening the way for better understanding of some disease causing mutations. The combination of this novel computational approach with other sources of information allowed us to provide a set of high-confidence novel interactions for the proteins here studied.},
	doi={10.1371/journal.pcbi.1000052},
	url={https://doi.org/10.1371/journal.pcbi.1000052}
}

@article{BU201825,
	title = "FoldX as Protein Engineering Tool: Better Than Random Based Approaches?",
	journal = "Computational and Structural Biotechnology Journal",
	volume = "16",
	pages = "25 - 33",
	year = "2018",
	issn = "2001-0370",
	doi = "https://doi.org/10.1016/j.csbj.2018.01.002",
	url = "http://www.sciencedirect.com/science/article/pii/S2001037017301186",
	author = "Oliver Buß and Jens Rudat and Katrin Ochsenreither",
	keywords = "FoldX, Fold-X, Thermostability, Protein stabilization, Protein engineering, Enzyme engineering",
	abstract = "Improving protein stability is an important goal for basic research as well as for clinical and industrial applications but no commonly accepted and widely used strategy for efficient engineering is known. Beside random approaches like error prone PCR or physical techniques to stabilize proteins, e.g. by immobilization, in silico approaches are gaining more attention to apply target-oriented mutagenesis. In this review different algorithms for the prediction of beneficial mutation sites to enhance protein stability are summarized and the advantages and disadvantages of FoldX are highlighted. The question whether the prediction of mutation sites by the algorithm FoldX is more accurate than random based approaches is addressed."
}

@Article{Alibes2010,
	author={Alib{\'e}s, Andreu
	and Nadra, Alejandro D.
	and De Masi, Federico
	and Bulyk, Martha L.
	and Serrano, Luis
	and Stricher, Fran{\c{c}}ois},
	title={Using protein design algorithms to understand the molecular basis of disease caused by protein-DNA interactions: the Pax6 example},
	journal={Nucleic Acids Research},
	year={2010},
	month={Aug},
	day={04},
	volume={38},
	number={21},
	pages={7422-7431},
	abstract={Quite often a single or a combination of protein mutations is linked to specific diseases. However, distinguishing from sequence information which mutations have real effects in the protein's function is not trivial. Protein design tools are commonly used to explain mutations that affect protein stability, or protein-protein interaction, but not for mutations that could affect protein-DNA binding. Here, we used the protein design algorithm FoldX to model all known missense mutations in the paired box domain of Pax6, a highly conserved transcription factor involved in eye development and in several diseases such as aniridia. The validity of FoldX to deal with protein-DNA interactions was demonstrated by showing that high levels of accuracy can be achieved for mutations affecting these interactions. Also we showed that protein-design algorithms can accurately reproduce experimental DNA-binding logos. We conclude that 88\% of the Pax6 mutations can be linked to changes in intrinsic stability (77\%) and/or to its capabilities to bind DNA (30\%). Our study emphasizes the importance of structure-based analysis to understand the molecular basis of diseases and shows that protein-DNA interactions can be analyzed to the same level of accuracy as protein stability, or protein-protein interactions.},
	issn={0305-1048},
	doi={10.1093/nar/gkq683},
	url={https://doi.org/10.1093/nar/gkq683}
}

@Article{Bu2018,
	author={Bu{\ss}, Oliver
	and Muller, Delphine
	and Jager, Sven
	and Rudat, Jens
	and Rabe, Kersten S.},
	title={Improvement in the Thermostability of a b-Amino Acid Converting o-Transaminase by Using FoldX},
	journal={ChemBioChem},
	year={2018},
	month={Feb},
	day={16},
	publisher={John Wiley \& Sons, Ltd},
	volume={19},
	number={4},
	pages={379-387},
	keywords={amino acids},
	keywords={biocatalysis},
	keywords={enzyme engineering},
	keywords={thermostability},
	keywords={transaminases},
	abstract={Abstract ?-Transaminases (?-TAs) are important biocatalysts for the synthesis of active, chiral pharmaceutical ingredients containing amino groups, such as ?-amino acids, which are important in peptidomimetics and as building blocks for drugs. However, the application of ?-TAs is limited by the availability and stability of enzymes with high conversion rates. One strategy for the synthesis and optical resolution of ?-phenylalanine and other important aromatic ?-amino acids is biotransformation by utilizing an ?-transaminase from Variovorax paradoxus. We designed variants of this ?-TA to gain higher process stability on the basis of predictions calculated by using the FoldX software. We herein report the first thermostabilization of a nonthermostable S-selective ?-TA by FoldX-guided site-directed mutagenesis. The melting point (Tm) of our best-performing mutant was increased to 59.3?{\textdegree}C, an increase of 4.0?{\textdegree}C relative to the Tm value of the wild-type enzyme, whereas the mutant fully retained its specific activity.},
	issn={1439-4227},
	doi={10.1002/cbic.201700467},
	url={https://doi.org/10.1002/cbic.201700467}
}

@incollection{NADRA20113,
	title = "Chapter one - DNA-Binding Specificity Prediction with FoldX",
	editor = "Christopher Voigt",
	series = "Methods in Enzymology",
	publisher = "Academic Press",
	volume = "498",
	pages = "3 - 18",
	year = "2011",
	booktitle = "Synthetic Biology, Part B",
	issn = "0076-6879",
	doi = "https://doi.org/10.1016/B978-0-12-385120-8.00001-2",
	url = "http://www.sciencedirect.com/science/article/pii/B9780123851208000012",
	author = "Alejandro D. Nadra and Luis Serrano and Andreu Alibés",
	abstract = "With the advent of Synthetic Biology, a field between basic science and applied engineering, new computational tools are needed to help scientists reach their goal, their design, optimizing resources. In this chapter, we present a simple and powerful method to either know the DNA specificity of a wild-type protein or design new specificities by using the protein design algorithm FoldX. The only basic requirement is having a good resolution structure of the complex. Protein–DNA interaction design may aid the development of new parts designed to be orthogonal, decoupled, and precise in its target. Further, it could help to fine-tune the systems in terms of specificity, discrimination, and binding constants. In the age of newly developed devices and invented systems, computer-aided engineering promises to be an invaluable tool."
}

@article{10.1093/protein/gzv004,

author = {Heselpoth, Ryan D. and Yin, Yizhou and Moult, John and Nelson, Daniel C.},

title = "{Increasing the stability of the bacteriophage endolysin PlyC using rationale-based FoldX computational modeling}",

journal = {Protein Engineering, Design and Selection},

volume = {28},

number = {4},

pages = {85-92},

year = {2015},

month = {03},

abstract = "{Endolysins are bacteriophage-derived peptidoglycan hydrolases that represent an emerging class of proteinaceous therapeutics. While the streptococcal endolysin PlyC has been validated in vitro and in vivo for its therapeutic efficacy, the inherent thermosusceptible structure of the enzyme correlates to transient long-term stability, thereby hindering the feasibility of developing the enzyme as an antimicrobial. Here, we thermostabilized the cysteine, histidine-dependent amidohydrolase/peptidase (CHAP) domain of the PlyCA catalytic subunit of PlyC using a FoldX-driven computational protein engineering approach. Using a combination of FoldX and Rosetta algorithms, as well as visual inspection, a final list of PlyC point mutant candidates with predicted stabilizing ΔΔG values was assembled and thermally characterized. Five of the eight point mutations were found experimentally to be destabilizing, a result most likely attributable to computationally modeling a complex and dynamic nine-subunit holoenzyme with a corresponding 3.3-Å X-ray crystal structure. However, one of the mutants, PlyC (PlyCA) T406R, was shown experimentally to increase the thermal denaturation temperature by ∼2.2°C and kinetic stability 16-fold over wild type. This mutation is expected to introduce a thermally advantageous hydrogen bond between the Q106 side chain of the N-terminal glycosyl hydrolase domain and the R406 side chain of the C-terminal CHAP domain.}",

issn = {1741-0126},

doi = {10.1093/protein/gzv004},

url = {https://doi.org/10.1093/protein/gzv004},

eprint = {http://oup.prod.sis.lan/peds/article-pdf/28/4/85/18547522/gzv004.pdf},

}

@article{capriotti2005mutant2,
	title={I-Mutant2. 0: predicting stability changes upon mutation from the protein sequence or structure},
	author={Capriotti, Emidio and Fariselli, Piero and Casadio, Rita},
	journal={Nucleic acids research},
	volume={33},
	number={suppl\_2},
	pages={W306--W310},
	year={2005},
	publisher={Oxford University Press}
}

@Article{Bava2004,
	author={Bava, K. Abdulla
	and Gromiha, M. Michael
	and Uedaira, Hatsuho
	and Kitajima, Koji
	and Sarai, Akinori},
	title={ProTherm, version 4.0: thermodynamic database for proteins and mutants},
	journal={Nucleic Acids Research},
	year={2004},
	month={Jan},
	day={01},
	volume={32},
	number={suppl\_1},
	pages={D120-D121},
	abstract={Release 4.0 of ProTherm, thermodynamic database for proteins and mutants, contains {\textasciitilde}14 500 numerical data ({\textasciitilde}450\% of the first version) of several thermodynamic parameters along with experimental methods and conditions, and structural, functional and literature information. The sequence and structural information of proteins is connected with thermodynamic data through links between entries in Protein Data Bank, Protein Information Resource and SWISS-PROT and the data in ProTherm. We have separated the Gibbs free energy change obtained at extrapolated temperature from the data on denaturation temperature measured by the thermal denaturation method. We have added the statistics of amino acid replacements and links to homologous structures to each protein. Further, we have improved the search and display options to enhance search capability through the web interface. ProTherm is freely available at http://gibk26. bse.kyutech.ac.jp/jouhou/Protherm/protherm.html .},
	issn={0305-1048},
	doi={10.1093/nar/gkh082},
	url={https://doi.org/10.1093/nar/gkh082}
}

@Article{Parthiban2006,
	author={Parthiban, Vijaya
	and Gromiha, M. Michael
	and Schomburg, Dietmar},
	title={CUPSAT: prediction of protein stability upon point mutations},
	journal={Nucleic Acids Research},
	year={2006},
	month={Jul},
	day={01},
	volume={34},
	number={suppl\_2},
	pages={W239-W242},
	abstract={CUPSAT (Cologne University Protein Stability Analysis Tool) is a web tool to analyse and predict protein stability changes upon point mutations (single amino acid mutations). This program uses structural environment specific atom potentials and torsion angle potentials to predict DDG, the difference in free energy of unfolding between wild-type and mutant proteins. It requires the protein structure in Protein Data Bank format and the location of the residue to be mutated. The output consists information about mutation site, its structural features (solvent accessibility, secondary structure and torsion angles), and comprehensive information about changes in protein stability for 19 possible substitutions of a specific amino acid mutation. Additionally, it also analyses the ability of the mutated amino acids to adapt the observed torsion angles. Results were tested on 1538 mutations from thermal denaturation and 1603 mutations from chemical denaturation experiments. Several validation tests (split-sample, jack-knife and k -fold) were carried out to ensure the reliability, accuracy and transferability of the prediction method that gives \>80\% prediction accuracy for most of these validation tests. Thus, the program serves as a valuable tool for the analysis of protein design and stability. The tool is accessible from the link http://cupsat.uni-koeln.de .},
	issn={0305-1048},
	doi={10.1093/nar/gkl190},
	url={https://doi.org/10.1093/nar/gkl190}
}

@Article{Zhou2002,
	author={Zhou, Hongyi
	and Zhou, Yaoqi},
	title={Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction},
	journal={Protein Science},
	year={2002},
	month={Nov},
	day={01},
	publisher={John Wiley \& Sons, Ltd},
	volume={11},
	number={11},
	pages={2714-2726},
	keywords={Knowledge-based potential},
	keywords={decoy sets},
	keywords={ideal-gas reference state},
	abstract={Abstract The distance-dependent structure-derived potentials developed so far all employed a reference state that can be characterized as a residue (atom)-averaged state. Here, we establish a new reference state called the distance-scaled, finite ideal-gas reference (DFIRE) state. The reference state is used to construct a residue-specific all-atom potential of mean force from a database of 1011 nonhomologous (less than 30\% homology) protein structures with resolution less than 2 {\AA}. The new all-atom potential recognizes more native proteins from 32 multiple decoy sets, and raises an average Z-score by 1.4 units more than two previously developed, residue-specific, all-atom knowledge-based potentials. When only backbone and C? atoms are used in scoring, the performance of the DFIRE-based potential, although is worse than that of the all-atom version, is comparable to those of the previously developed potentials on the all-atom level. In addition, the DFIRE-based all-atom potential provides the most accurate prediction of the stabilities of 895 mutants among three knowledge-based all-atom potentials. Comparison with several physical-based potentials is made.},
	issn={0961-8368},
	doi={10.1110/ps.0217002},
	url={https://doi.org/10.1110/ps.0217002}
}

@article{chawla2002smote,
	title={SMOTE: synthetic minority over-sampling technique},
	author={Chawla, Nitesh V and Bowyer, Kevin W and Hall, Lawrence O and Kegelmeyer, W Philip},
	journal={Journal of artificial intelligence research},
	volume={16},
	pages={321--357},
	year={2002}
}

@article{berman2000protein,
	title={The protein data bank},
	author={Berman, Helen M and Westbrook, John and Feng, Zukang and Gilliland, Gary and Bhat, Talapady N and Weissig, Helge and Shindyalov, Ilya N and Bourne, Philip E},
	journal={Nucleic acids research},
	volume={28},
	number={1},
	pages={235--242},
	year={2000},
	publisher={Oxford University Press}
}

@article{doi:10.1109/MCSE.2011.37,
	author = {Walt,Stéfan van der  and Colbert,S. Chris  and Varoquaux,Gaël },
	title = {The NumPy Array: A Structure for Efficient Numerical Computation},
	journal = {Computing in Science \& Engineering},
	volume = {13},
	number = {2},
	pages = {22-30},
	year = {2011},
	doi = {10.1109/MCSE.2011.37},

	URL = {
	https://aip.scitation.org/doi/abs/10.1109/MCSE.2011.37

	},
	eprint = {
	https://aip.scitation.org/doi/pdf/10.1109/MCSE.2011.37

	}

}

@Article{Wainreb2011,
	author={Wainreb, Gilad
	and Ashkenazy, Haim
	and Wolf, Lior
	and Ben-Tal, Nir
	and Dehouck, Yves},
	title={Protein stability: a single recorded mutation aids in predicting the effects of other mutations in the same amino acid site},
	journal={Bioinformatics},
	year={2011},
	month={Oct},
	day={13},
	volume={27},
	number={23},
	pages={3286-3292},
	abstract={Motivation: Accurate prediction of protein stability is important for understanding the molecular underpinnings of diseases and for the design of new proteins. We introduce a novel approach for the prediction of changes in protein stability that arise from a single-site amino acid substitution; the approach uses available data on mutations occurring in the same position and in other positions. Our algorithm, named Pro-Maya (Protein Mutant stAbilitY Analyzer), combines a collaborative filtering baseline model, Random Forests regression and a diverse set of features. Pro-Maya predicts the stability free energy difference of mutant versus wild type, denoted as DDG.Results: We evaluated our algorithm extensively using cross-validation on two previously utilized datasets of single amino acid mutations and a (third) validation set. The results indicate that using known DDG values of mutations at the query position improves the accuracy of DDG predictions for other mutations in that position. The accuracy of our predictions in such cases significantly surpasses that of similar methods, achieving, e.g. a Pearson's correlation coefficient of 0.79 and a root mean square error of 0.96 on the validation set. Because Pro-Maya uses a diverse set of features, including predictions using two other methods, it also performs slightly better than other methods in the absence of additional experimental data on the query positions.Availability: Pro-Maya is freely available via web server at http://bental.tau.ac.il/ProMaya.Contact:nirb@tauex.tau.ac.il; wolf@cs.tau.ac.ilSupplementary Information:Supplementary data are available at Bioinformatics online.},
	issn={1367-4803},
	doi={10.1093/bioinformatics/btr576},
	url={https://doi.org/10.1093/bioinformatics/btr576}
}

@incollection{abola1984protein,
	title={The protein data bank},
	author={Abola, Enrique E and Bernstein, Frances C and Koetzle, Thomas F},
	booktitle={Neutrons in Biology},
	pages={441--441},
	year={1984},
	publisher={Springer}
}

@article{hand2006data,
	title={Data Mining},
	author={Hand, David J},
	journal={Encyclopedia of Environmetrics},
	volume={2},
	year={2006},
	publisher={Wiley Online Library}
}

@inproceedings{fayyad1996knowledge,
	title={Knowledge Discovery and Data Mining: Towards a Unifying Framework.},
	author={Fayyad, Usama M and Piatetsky-Shapiro, Gregory and Smyth, Padhraic and others},
	booktitle={KDD},
	volume={96},
	pages={82--88},
	year={1996}
}

@book{dunham2006data,
	title={Data mining: Introductory and advanced topics},
	author={Dunham, Margaret H},
	year={2006},
	publisher={Pearson Education India}
}

@article{fayyad1996kdd,
	title={The KDD process for extracting useful knowledge from volumes of data},
	author={Fayyad, Usama and Piatetsky-Shapiro, Gregory and Smyth, Padhraic},
	journal={Communications of the ACM},
	volume={39},
	number={11},
	pages={27--34},
	year={1996},
	publisher={ACM}
}

@book{berry2004data,
	title={Data mining techniques: for marketing, sales, and customer relationship management},
	author={Berry, Michael JA and Linoff, Gordon S},
	year={2004},
	publisher={John Wiley \& Sons}
}

@book{hofmann2013rapidminer,
	title={RapidMiner: Data mining use cases and business analytics applications},
	author={Hofmann, Markus and Klinkenberg, Ralf},
	year={2013},
	publisher={CRC Press}
}

@Article{Ma2013,
	author={Ma, Xiaolei
	and Wu, Yao-Jan
	and Wang, Yinhai
	and Chen, Feng
	and Liu, Jianfeng},
	title={Mining smart card data for transit riders' travel patterns},
	journal={Transportation Research Part C: Emerging Technologies},
	year={2013},
	volume={36},
	pages={1-12},
	keywords={Automatic Fare Collection System},
	keywords={Smart card},
	keywords={Transit travel pattern},
	keywords={K-Means algorithm},
	keywords={Rough set theory},
	abstract={To mitigate the congestion caused by the ever increasing number of privately owned automobiles, public transit is highly promoted by transportation agencies worldwide. A better understanding of travel patterns and regularity at the "magnitude" level will enable transit authorities to evaluate the services they offer, adjust marketing strategies, retain loyal customers and improve overall transit performance. However, it is fairly challenging to identify travel patterns for individual transit riders in a large dataset. This paper proposes an efficient and effective data-mining procedure that models the travel patterns of transit riders in Beijing, China. Transit riders' trip chains are identified based on the temporal and spatial characteristics of their smart card transaction data. The Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm then analyzes the identified trip chains to detect transit riders' historical travel patterns and the K-Means++ clustering algorithm and the rough-set theory are jointly applied to cluster and classify travel pattern regularities. The performance of the rough-set-based algorithm is compared with those of other prevailing classification algorithms. The results indicate that the proposed rough-set-based algorithm outperforms other commonly used data-mining algorithms in terms of accuracy and efficiency.},
	issn={0968-090X},
	url={http://www.sciencedirect.com/science/article/pii/S0968090X13001630}
}

@Article{Lee2008,
	author={Lee, Jae K.
	and Williams, Paul D.
	and Cheon, Sooyoung},
	title={Data Mining in Genomics},
	journal={Clinics in Laboratory Medicine},
	year={2008},
	volume={28},
	number={1},
	pages={145-166},
	abstract={This article reviews important emerging statistical concepts, data mining techniques, and applications that have been recently developed and used for genomic data analysis. First, general background and some critical issues in genomic data mining are summarized. A novel concept of statistical significance is described, the so-called "false discovery rate"--the rate of false-positives among all positive findings--which has been suggested to control the error rate of numerous false-positives in large screening biological data analysis. Two recent statistical testing methods are then introduced: significance analysis of microarray and local pooled error tests. Statistical modeling in genomic data analysis is then presented, such as analysis of variance and heterogeneous error modeling approaches that have been suggested for analyzing microarray data obtained from multiple experimental or biological conditions. Two sections then describe data exploration and discovery tools largely termed as supervised learning and unsupervised learning. The former approaches include several multivariate statistical methods to investigate coexpression patterns of multiple genes, and the latter are the classification methods to discover genomic biomarker signatures for predicting important subclasses of human diseases. The last section briefly summarizes various genomic data mining approaches in biomedical pathway analysis and patient outcome or chemotherapeutic response