text:
prediction.},
	issn={0272-2712},
	url={http://www.sciencedirect.com/science/article/pii/S0272271207001199}
}

@Article{Rebhan1998,
	author={Rebhan, M.
	and Chalifa-Caspi, V.
	and Prilusky, J.
	and Lancet, D.},
	title={GeneCards: a novel functional genomics compendium with automated data mining and query reformulation support.},
	journal={Bioinformatics},
	year={1998},
	month={Sep},
	day={01},
	volume={14},
	number={8},
	pages={656-664},
	abstract={MOTIVATION: Modern biology is shifting from the 'one gene one postdoc' approach to genomic analyses that include the simultaneous monitoring of thousands of genes. The importance of efficient access to concise and integrated biomedical information to support data analysis and decision making is therefore increasing rapidly, in both academic and industrial research. However, knowledge discovery in the widely scattered resources relevant for biomedical research is often a cumbersome and non-trivial task, one that requires a significant amount of training and effort. RESULTS: To develop a model for a new type of topic-specific overview resource that provides efficient access to distributed information, we designed a database called 'GeneCards'. It is a freely accessible Web resource that offers one hypertext 'card' for each of the more than 7000 human genes that currently have an approved gene symbol published by the HUGO/GDB nomenclature committee. The presented information aims at giving immediate insight into current knowledge about the respective gene, including a focus on its functions in health and disease. It is compiled by Perl scripts that automatically extract relevant information from several databases, including SWISS-PROT, OMIM, Genatlas and GDB. Analyses of the interactions of users with the Web interface of GeneCards triggered development of easy-to-scan displays optimized for human browsing. Also, we developed algorithms that offer 'ready-to-click' query reformulation support, to facilitate information retrieval and exploration. Many of the long-term users turn to GeneCards to quickly access information about the function of very large sets of genes, for example in the realm of large-scale expression studies using 'DNA chip' technology or two-dimensional protein electrophoresis. AVAILABILITY: Freely available at http://bioinformatics.weizmann.ac.il/cards/ Contact: cards@bioinformatics.weizmann.ac.il},
	issn={1367-4803},
	doi={10.1093/bioinformatics/14.8.656},
	url={https://doi.org/10.1093/bioinformatics/14.8.656}
}

@article{han2009research,
	title={Research challenges for data mining in science and engineering},
	author={Han, Jiawei and Gao, Jing},
	journal={Next Generation of Data Mining},
	pages={1--18},
	year={2009},
	publisher={Chapman \& Hall}
}

@INPROCEEDINGS{4548625,
	author={M. {Li} and J. {Wang} and J. {Chen}},
	booktitle={2008 International Conference on BioMedical Engineering and Informatics},
	title={A Fast Agglomerate Algorithm for Mining Functional Modules in Protein Interaction Networks},
	year={2008},
	volume={1},
	number={},
	pages={3-7},
	keywords={biology computing;data mining;expert systems;molecular biophysics;proteins;mining functional modules;protein interaction networks;protein-protein interaction;cellular organization;fast agglomerate algorithm;edge clustering coefficients;FAG-EC;core protein interaction network;budding yeast;Clustering algorithms;Iterative algorithms;Cellular networks;Biomedical engineering;Protein engineering;Fungi;Biomedical informatics;Information science;Data engineering;Electronics packaging},
	doi={10.1109/BMEI.2008.121},
	ISSN={1948-2914},
	month={May}
}

@inproceedings{li2008fast,
	title={A fast agglomerate algorithm for mining functional modules in protein interaction networks},
	author={Li, Min and Wang, Jianxin and Chen, Jian'er},
	booktitle={2008 International conference on biomedical engineering and informatics},
	volume={1},
	pages={3--7},
	year={2008},
	organization={IEEE}
}

@Article{Chien2008,
	author={Chien, Chen-Fu
	and Chen, Li-Fei},
	title={Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry},
	journal={Expert Systems with Applications},
	year={2008},
	volume={34},
	number={1},
	pages={280-290},
	keywords={Personnel selection},
	keywords={Human capital},
	keywords={Data mining},
	keywords={Decision tree},
	keywords={Semiconductor industry},
	abstract={The quality of human capital is crucial for high-tech companies to maintain competitive advantages in knowledge economy era. However, high-technology companies suffering from high turnover rates often find it hard to recruit the right talents. In addition to conventional human resource management approaches, there is an urgent need to develop effective personnel selection mechanism to find the talents who are the most suitable to their own organizations. This study aims to fill the gap by developing a data mining framework based on decision tree and association rules to generate useful rules for personnel selection. The results can provide decision rules relating personnel information with work performance and retention. An empirical study was conducted in a semiconductor company to support their hiring decision for indirect labors including engineers and managers with different job functions. The results demonstrated the practical viability of this approach. Moreover, based on discussions among domain experts and data miner, specific recruitment and human resource management strategies were created from the results.},
	issn={0957-4174},
	url={http://www.sciencedirect.com/science/article/pii/S0957417406002776}
}

@ARTICLE{8051033,
	author={Z. {Ge} and Z. {Song} and S. X. {Ding} and B. {Huang}},
	journal={IEEE Access},
	title={Data Mining and Analytics in the Process Industry: The Role of Machine Learning},
	year={2017},
	volume={5},
	number={},
	pages={20590-20616},
	keywords={data analysis;data mining;decision making;learning (artificial intelligence);pattern recognition;unsupervised learning;process industry;machine learning;data mining;data pattern recognition;analytics applications;data analytics;knowledge discovery;decision making;decision supports;information extraction;unsupervised learning;supervised learning;Data mining;Industries;Data models;Machine learning algorithms;Analytical models;Manufacturing;Predictive models;Data mining;data analytics;machine learning;process industry},
	doi={10.1109/ACCESS.2017.2756872},
	ISSN={2169-3536},
	month={}
}

@ARTICLE{983448,
	author={D. {Braha} and A. {Shmilovici}},
	journal={IEEE Transactions on Semiconductor Manufacturing},
	title={Data mining for improving a cleaning process in the semiconductor industry},
	year={2002},
	volume={15},
	number={1},
	pages={91-101},
	keywords={surface contamination;semiconductor technology;surface cleaning;integrated circuit yield;integrated circuit technology;data mining;manufacturing data processing;classification;neural nets;decision trees;electronic engineering computing;data mining;cleaning process;semiconductor industry;device geometry;micro-contaminants;wafer yield;wafer contamination;semiconductor manufacturers;data mining methodologies;dry cleaning technology;laser beam micro-contaminant removal;classification-based data mining methods;decision tree induction;neural networks;composite classifiers;composite classifier architecture;chemical parameters;physical parameters;process interactions;on-line monitoring;laser cleaning;machine learning;Data mining;Cleaning;Classification tree analysis;Geometrical optics;Contamination;Semiconductor device manufacture;Refining;Semiconductor lasers;Laser beams;Decision trees},
	doi={10.1109/66.983448},
	ISSN={0894-6507},
	month={Feb}
}

@book{jain1988algorithms,
	title={Algorithms for clustering data},
	author={Jain, Anil K and Dubes, Richard C and others},
	volume={6},
	year={1988},
	publisher={Prentice hall Englewood Cliffs}
}

@article{fayyad1996data,
	title={From data mining to knowledge discovery in databases},
	author={Fayyad, Usama and Piatetsky-Shapiro, Gregory and Smyth, Padhraic},
	journal={AI magazine},
	volume={17},
	number={3},
	pages={37--37},
	year={1996}
}

@Article{Yoo2012,
	author="Yoo, Illhoi
	and Alafaireet, Patricia
	and Marinov, Miroslav
	and Pena-Hernandez, Keila
	and Gopidi, Rajitha
	and Chang, Jia-Fu
	and Hua, Lei",
	title="Data Mining in Healthcare and Biomedicine: A Survey of the Literature",
	journal="Journal of Medical Systems",
	year="2012",
	month="Aug",
	day="01",
	volume="36",
	number="4",
	pages="2431--2448",
	issn="1573-689X",
	doi="10.1007/s10916-011-9710-5",
	url="https://doi.org/10.1007/s10916-011-9710-5"
}

@article{obenshain2004,
	title={Application of Data Mining Techniques to Healthcare Data},
	volume={25},
	DOI={10.1086/502460},
	number={8},
	journal={Infection Control \&; Hospital Epidemiology},
	publisher={Cambridge University Press},
	author={Obenshain, Mary K.},
	year={2004},
	pages={690–695}
}

@article{LDuan,
	author = { L.   Duan  and  W. N.   Street  and  E.   Xu },
	title = {Healthcare information systems: data mining methods in the creation of a clinical recommender system},
	journal = {Enterprise Information Systems},
	volume = {5},
	number = {2},
	pages = {169-181},
	year  = {2011},
	publisher = {Taylor & Francis},
	doi = {10.1080/17517575.2010.541287},

	URL = {
	https://doi.org/10.1080/17517575.2010.541287

	},
	eprint = {
	https://doi.org/10.1080/17517575.2010.541287

	}

}

@article{MAO2004787,
	title = "Molecular Determinants for ATP-binding in Proteins: A Data Mining and Quantum Chemical Analysis",
	journal = "Journal of Molecular Biology",
	volume = "336",
	number = "3",
	pages = "787 - 807",
	year = "2004",
	issn = "0022-2836",
	doi = "https://doi.org/10.1016/j.jmb.2003.12.056",
	url = "http://www.sciencedirect.com/science/article/pii/S0022283603015754",
	author = "Lisong Mao and Yanli Wang and Yuemin Liu and Xiche Hu",
	keywords = "ATP-binding proteins, intermolecular interactions, hydrogen bonds, cation–π interactions, π–π stacking interactions",
	abstract = "Adenosine 5′-triphosphate (ATP) plays an essential role in all forms of life. Molecular recognition of ATP in proteins is a subject of great importance for understanding enzymatic mechanism and for drug design. We have carried out a large-scale data mining of the Protein Data Bank (PDB) to analyze molecular determinants for recognition of the adenine moiety of ATP by proteins. Non-bonded intermolecular interactions (hydrogen bonding, π–π stacking interactions, and cation–π interactions) between adenine base and surrounding residues in its binding pockets are systematically analyzed for 68 non-redundant, high-resolution crystal structures of adenylate-binding proteins. In addition to confirming the importance of the widely known hydrogen bonding, we found out that cation–π interactions between adenine base and positively charged residues (Lys and Arg) and π–π stacking interactions between adenine base and surrounding aromatic residues (Phe, Tyr, Trp) are also crucial for adenine binding in proteins. On average, there exist 2.7 hydrogen bonding interactions, 1.0 π–π stacking interactions, and 0.8 cation–π interactions in each adenylate-binding protein complex. Furthermore, a high-level quantum chemical analysis was performed to analyze contributions of each of the three forms of intermolecular interactions (i.e. hydrogen bonding, π–π stacking interactions, and cation–π interactions) to the overall binding force of the adenine moiety of ATP in proteins. Intermolecular interaction energies for representative configurations of intermolecular complexes were analyzed using the supermolecular approach at the MP2/6-311+G∗ level, which resulted in substantial interaction strengths for all the three forms of intermolecular interactions. This work represents a timely undertaking at a historical moment when a large number of X-ray crystallographic structures of proteins with bound ATP ligands have become available, and when high-level quantum chemical analysis of intermolecular interactions of large biomolecular systems becomes computationally feasible. The establishment of the molecular basis for recognition of the adenine moiety of ATP in proteins will directly impact molecular design of ATP-binding site targeted enzyme inhibitors such as kinase inhibitors."
}

@article{PhysRevLett.91.135503,
	title = {Predicting Crystal Structures with Data Mining of Quantum Calculations},
	author = {Curtarolo, Stefano and Morgan, Dane and Persson, Kristin and Rodgers, John and Ceder, Gerbrand},
	journal = {Phys. Rev. Lett.},
	volume = {91},
	issue = {13},
	pages = {135503},
	numpages = {4},
	year = {2003},
	month = {Sep},
	publisher = {American Physical Society},
	doi = {10.1103/PhysRevLett.91.135503},
	url = {https://link.aps.org/doi/10.1103/PhysRevLett.91.135503}
}

@Article{Ramakrishnan2015,
	author={Ramakrishnan, Raghunathan
	and Dral, Pavlo O.
	and Rupp, Matthias
	and von Lilienfeld, O. Anatole},
	title={Big Data Meets Quantum Chemistry Approximations: The D-Machine Learning Approach},
	journal={Journal of Chemical Theory and Computation},
	year={2015},
	month={May},
	day={12},
	publisher={American Chemical Society},
	volume={11},
	number={5},
	pages={2087-2096},
	issn={1549-9618},
	doi={10.1021/acs.jctc.5b00099},
	url={https://doi.org/10.1021/acs.jctc.5b00099}
}

@Article{Chen2017,
	author={Chen, Chi
	and Lu, Ziheng
	and Ciucci, Francesco},
	title={Data mining of molecular dynamics data reveals Li diffusion characteristics in garnet Li7La3Zr2O12},
	journal={Scientific Reports},
	year={2017},
	month={Jan},
	day={17},
	publisher={The Author(s) SN  -},
	volume={7},
	pages={40769 EP  -},
	note={Article},
	url={https://doi.org/10.1038/srep40769}
}

@inproceedings{Yang:2005:GFM:1081870.1081962,
	author = {Yang, Hui and Parthasarathy, Srinivasan and Mehta, Sameep},
	title = {A Generalized Framework for Mining Spatio-temporal Patterns in Scientific Data},
	booktitle = {Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining},
	series = {KDD '05},
	year = {2005},
	isbn = {1-59593-135-X},
	location = {Chicago, Illinois, USA},
	pages = {716--721},
	numpages = {6},
	url = {http://doi.acm.org/10.1145/1081870.1081962},
	doi = {10.1145/1081870.1081962},
	acmid = {1081962},
	publisher = {ACM},
	address = {New York, NY, USA},
	keywords = {scientific data, spatial object association, spatio-temporal association/episode},
}

@InProceedings{10.1007/BFb0026666,
	author="Lewis, David D.",
	editor="N{\'e}dellec, Claire
	and Rouveirol, C{\'e}line",
	title="Naive (Bayes) at forty: The independence assumption in information retrieval",
	booktitle="Machine Learning: ECML-98",
	year="1998",
	publisher="Springer Berlin Heidelberg",
	address="Berlin, Heidelberg",
	pages="4--15",
	abstract="The naive Bayes classifier, currently experiencing a renaissance ] in machine learning, has long been a core technique in information retrieval. We review some of the variations of naive Bayes models used for text retrieval and classification, focusing on the distributional assumptions made about word occurrences in documents.",
	isbn="978-3-540-69781-7"
}

@Article{Chen2009,
	author={Chen, Jingnian
	and Huang, Houkuan
	and Tian, Shengfeng
	and Qu, Youli},
	title={Feature selection for text classification with Na{\"i}ve Bayes},
	journal={Expert Systems with Applications},
	year={2009},
	volume={36},
	number={3, Part 1},
	pages={5432-5435},
	keywords={Text classification},
	keywords={Feature selection},
	keywords={Text preprocessing},
	keywords={Na{\"i}ve Bayes},
	abstract={As an important preprocessing technology in text classification, feature selection can improve the scalability, efficiency and accuracy of a text classifier. In general, a good feature selection method should consider domain and algorithm characteristics. As the Na{\"i}ve Bayesian classifier is very simple and efficient and highly sensitive to feature selection, so the research of feature selection specially for it is significant. This paper presents two feature evaluation metrics for the Na{\"i}ve Bayesian classifier applied on multi-class text datasets: Multi-class Odds Ratio (MOR), and Class Discriminating Measure (CDM). Experiments of text classification with Na{\"i}ve Bayesian classifiers were carried out on two multi-class texts collections. As the results indicate, CDM and MOR gain obviously better selecting effect than other feature selection approaches.},
	issn={0957-4174},
	url={http://www.sciencedirect.com/science/article/pii/S0957417408003564}
}

@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}
}

@article{fisher1936use,
	title={The use of multiple measurements in taxonomic problems},
	author={Fisher, Ronald A},
	journal={Annals of eugenics},
	volume={7},
	number={2},
	pages={179--188},
	year={1936},
	publisher={Wiley Online Library}
}

@article{battiti1992first,
	title={First-and second-order methods for learning: between steepest descent and Newton's method},
	author={Battiti, Roberto},
	journal={Neural computation},
	volume={4},
	number={2},
	pages={141--166},
	year={1992},
	publisher={MIT Press}
}

@article{golub1979generalized,
	title={Generalized cross-validation as a method for choosing a good ridge parameter},
	author={Golub, Gene H and Heath, Michael and Wahba, Grace},
	journal={Technometrics},
	volume={21},
	number={2},
	pages={215--223},
	year={1979},
	publisher={Taylor \& Francis Group}
}

@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}
}

@article{vehtari2017practical,
	title={Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC},
	author={Vehtari, Aki and Gelman, Andrew and Gabry, Jonah},
	journal={Statistics and Computing},
	volume={27},
	number={5},
	pages={1413--1432},
	year={2017},
	publisher={Springer}
}

@Article{Masso2010,
	author={Masso, Majid
	and Vaisman, Iosif I.},
	title={AUTO-MUTE: web-based tools for predicting stability changes in proteins due to single amino acid replacements},
	journal={Protein Engineering, Design and Selection},
	year={2010},
	month={Jun},
	day={23},
	volume={23},
	number={8},
	pages={683-687},
	abstract={Utilizing cutting-edge supervised classification and regression algorithms, three web-based tools have been developed for predicting stability changes upon single residue substitutions in proteins with known native structures. Trained models classify independent mutant test sets with accuracies ranging from 87 to 94\%. Attributes representing each mutant protein are based on a computational mutagenesis methodology relying on a four-body statistical potential, illustrating a novel integration of both energy-based and machine learning approaches. The servers are written in PHP and hosted on a Linux platform, and they can be freely accessed online along with detailed data sets, documentation and performance results at http://proteins.gmu.edu/automute.},
	issn={1741-0126},
	doi={10.1093/protein/gzq042},
	url={https://doi.org/10.1093/protein/gzq042}
}

@Article{Zhou2004,
	author={Zhou, Hongyi
	and Zhou, Yaoqi},
	title={Quantifying the effect of burial of amino acid residues on protein stability},
	journal={Proteins: Structure, Function, and Bioinformatics},
	year={2004},
	month={Feb},
	day={01},
	publisher={John Wiley \& Sons, Ltd},
	volume={54},
	number={2},
	pages={315-322},
	keywords={hydrophobicity scale},
	keywords={knowledge-based potential},
	keywords={buriability},
	keywords={stability scale},
	abstract={Abstract The average contribution of individual residue to folding stability and its dependence on buried accessible surface area (ASA) are obtained by two different approaches. One is based on experimental mutation data, and the other uses a new knowledge-based atom?atom potential of mean force. We show that the contribution of a residue has a significant correlation with buried ASA and the regression slopes of 20 amino acid residues (called the buriability) are all positive (pro-burial). The buriability parameter provides a quantitative measure of the driving force for the burial of a residue. The large buriability gap observed between hydrophobic and hydrophilic residues is responsible for the burial of hydrophobic residues in soluble proteins. Possible factors that contribute to the buriability gap are discussed. Proteins 2004;54:000?000. ? 2003 Wiley-Liss, Inc.},
	issn={0887-3585},
	doi={10.1002/prot.10584},
	url={https://doi.org/10.1002/prot.10584}
}

@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}
}

@book{graybill1976theory,
	title={Theory and application of the linear model},
	author={Graybill, Franklin A},
	number={QA279. G72 1976.},
	year={1976},
	publisher={Duxbury press North Scituate, MA}
}

@article{golub1980analysis,
	title={An analysis of the total least squares problem},
	author={Golub, Gene H and Van Loan, Charles F},
	journal={SIAM journal on numerical analysis},
	volume={17},
	number={6},
	pages={883--893},
	year={1980},
	publisher={SIAM}
}

@article{hoerl1970ridge,
	title={Ridge regression: Biased estimation for nonorthogonal problems},
	author={Hoerl, Arthur E and Kennard, Robert W},
	journal={Technometrics},
	volume={12},
	number={1},
	pages={55--67},
	year={1970},
	publisher={Taylor \& Francis Group}
}

@article{hans2009bayesian,
	title={Bayesian lasso regression},
	author={Hans, Chris},
	journal={Biometrika},
	volume={96},
	number={4},
	pages={835--845},
	year={2009},
	publisher={Oxford University Press}
}

@article{zou2005regularization,
	title={Regularization and variable selection via the elastic net},
	author={Zou, Hui and Hastie, Trevor},
	journal={Journal of the royal statistical society: series B (statistical methodology)},
	volume={67},
	number={2},
	pages={301--320},
	year={2005},
	publisher={Wiley Online Library}
}

@inproceedings{pati1993orthogonal,
	title={Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition},
	author={Pati, Yagyensh Chandra and Rezaiifar, Ramin and Krishnaprasad, Perinkulam Sambamurthy},
	booktitle={Proceedings of 27th Asilomar conference on signals, systems and computers},
	pages={40--44},
	year={1993},
	organization={IEEE}
}

@article{castillo2015bayesian,
	title={Bayesian linear regression with sparse priors},
	author={Castillo, Isma{\"e}l and Schmidt-Hieber, Johannes and Van der Vaart, Aad and others},
	journal={The Annals of Statistics},
	volume={43},
	number={5},
	pages={1986--2018},
	year={2015},
	publisher={Institute of Mathematical Statistics}
}

@article{hssina2014comparative,
	title={A comparative study of decision tree ID3 and C4. 5},
	author={Hssina, Badr and Merbouha, Abdelkarim and Ezzikouri, Hanane and Erritali, Mohammed},
	journal={International Journal of Advanced Computer Science and Applications},
	volume={4},
	number={2},
	pages={0--0},
	year={2014},
	publisher={Citeseer}
}

@book{lutz2009protein,
	title={Protein engineering handbook},
	author={Lutz, Stefan and Bornscheuer, Uwe Theo and others},
	volume={1},
	year={2009},
	publisher={Wiley Online Library}
}

@article{carpenter1997rational,
	title={Rational design of stable lyophilized protein formulations: some practical advice},
	author={Carpenter, John F and Pikal, Michael J and Chang, Byeong S and Randolph, Theodore W},
	journal={Pharmaceutical research},
	volume={14},
	number={8},
	pages={969--975},
	year={1997},
	publisher={Springer}
}

@article{arnold1998design,
	title={Design by directed evolution},
	author={Arnold, Frances H},
	journal={Accounts of chemical research},
	volume={31},
	number={3},
	pages={125--131},
	year={1998},
	publisher={ACS Publications}
}

@Article{Reetz2008,
	author={Reetz, Manfred T.
	and Kahakeaw, Daniel
	and Lohmer, Renate},
	title={Addressing the Numbers Problem in Directed Evolution},
	journal={ChemBioChem},
	year={2008},
	month={Jul},
	day={21},
	publisher={John Wiley \& Sons, Ltd},
	volume={9},
	number={11},
	pages={1797-1804},
	keywords={codon degeneracy},
	keywords={enantioselectivity},
	keywords={enzymes},
	keywords={epoxides},
	keywords={hydrolases},
	keywords={saturation mutagenesis},
	abstract={Abstract Our previous contribution to increasing the efficiency of directed evolution is iterative saturation mutagenesis (ISM) as a systematic means of generating focused libraries for the control of substrate acceptance, enantioselectivity, or thermostability of enzymes. We have now introduced a crucial element to knowledge-guided targeted mutagenesis in general that helps to solve the numbers problem in directed evolution. We show that the choice of the amino acid (aa) alphabet, as specified by the utilized codon degeneracy, provides the experimenter with a powerful tool in designing ?smarter? randomized libraries that require considerably less screening effort. A systematic comparison of two different codon degeneracies was made by examining the relative quality of the identically sized enzyme libraries in relation to the degree of oversampling required in the screening process. The specific example in our case study concerns the conventional NNK codon degeneracy (32 codons/20 aa) versus NDT (12 codons/12 aa). The model reaction is the hydrolytic kinetic resolution of a chiral trans-disubstituted epoxide, catalyzed by the epoxide hydrolase from Aspergillus niger. The NDT library proves to be of much higher quality, as measured by the dramatically higher frequency of positive variants and by the magnitude of catalyst improvement (enhanced rate and enantioselectivity). We provide a statistical analysis that constitutes a useful guide for the optimal design and generation of ?smarter? focused libraries. This type of approach accelerates the process of laboratory evolution considerably and can be expected to be broadly applicable when engineering functional proteins in general.},
	issn={1439-4227},
	doi={10.1002/cbic.200800298},
	url={https://doi.org/10.1002/cbic.200800298}
}

@Article{Kawashima2000,
	author={Kawashima, Shuichi
	and Kanehisa, Minoru},
	title={AAindex: Amino Acid index database},
	journal={Nucleic Acids Research},
	year={2000},
	month={Jan},
	day={01},
	volume={28},
	number={1},
	pages={374-374},
	abstract={AAindex is a database of amino acid indices and amino acid mutation matrices. An amino acid index is a set of 20 numerical values representing various physico?-chemical and biochemical properties of amino acids. An amino acid mutation matrix is generally 20 {\texttimes} 20 numerical values representing similarity of amino acids. AAindex consists of two sections: AAindex1 for the collection of published amino acid indices and AAindex2 for the collection of published amino acid mutation matrices. Each entry of either AAindex1 or AAindex2 consists of the definition, the reference information, a list of related entries in terms of the correlation coefficient and the actual data. The database may be accessed through the DBGET/LinkDB system at GenomeNet (http://www.genome.ad.jp/aaindex/ ) or may be downloaded by anonymous FTP (ftp://ftp.genome.ad.jp/db/genomenet/aaindex/ ).},
	issn={0305-1048},
	doi={10.1093/nar/28.1.374},
	url={https://doi.org/10.1093/nar/28.1.374}
}

@article{krasner1988description,
	title={A description of the model-view-controller user interface paradigm in the smalltalk-80 system},
	author={Krasner, Glenn E and Pope, Stephen T and others},
	journal={Journal of object oriented programming},
	volume={1},
	number={3},
	pages={26--49},
	year={1988}
}

@article{wegner1990concepts,
	title={Concepts and paradigms of object-oriented programming},
	author={Wegner, Peter},
	journal={ACM Sigplan Oops Messenger},
	volume={1},
	number={1},
	pages={7--87},
	year={1990},
	publisher={ACM}
}

@book{brigham1988fast,
	title={The fast Fourier transform and its applications},
	author={Brigham, E Oran and Brigham, E Oran},
	volume={448},
	year={1988},
	publisher={prentice Hall Englewood Cliffs, NJ}
}

@article{oliphant2007python,
	title={Python for scientific computing},
	author={Oliphant, Travis E},
	journal={Computing in Science \& Engineering},
	volume={9},
	number={3},
	pages={10--20},
	year={2007},
	publisher={IEEE}
}

@article{van2011numpy,
	title={The NumPy array: a structure for efficient numerical computation},
	author={Van Der Walt, Stefan and Colbert, S Chris and Varoquaux, Gael},
	journal={Computing in Science \& Engineering},
	volume={13},
	number={2},
	pages={22},
	year={2011},
	publisher={IEEE Computer Society}
}

@inproceedings{mckinney2010data,
	title={Data structures for statistical computing in python},
	author={McKinney, Wes and others},
	booktitle={Proceedings of the 9th Python in Science Conference},
	volume={445},
	pages={51--56},
	year={2010},
	organization={Austin, TX}
}

@phdthesis{schmidhuber1987evolutionary,
	title={Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-... hook},
	author={Schmidhuber, J{\"u}rgen},
	year={1987},
	school={Technische Universit{\"a}t M{\"u}nchen}
}

@article{utgoff1986shift,
	title={Shift of bias for inductive concept learning},
	author={Utgoff, Paul E},
	journal={Machine learning: An artificial intelligence approach},
	volume={2},
	pages={107--148},
	year={1986},
	publisher={RS Michalski, JG Carbonell, and TM Mitchell. California: Morgan Kaufmann}
}

@inproceedings{hochreiter2001learning,
	title={Learning to learn using gradient descent},
	author={Hochreiter, Sepp and Younger, A Steven and Conwell, Peter R},
	booktitle={International Conference on Artificial Neural Networks},
	pages={87--94},
	year={2001},
	organization={Springer}
}

@inproceedings{andrychowicz2016learning,
	title={Learning to learn by gradient descent by gradient descent},
	author={Andrychowicz, Marcin and Denil, Misha and Gomez, Sergio and Hoffman, Matthew W and Pfau, David and Schaul, Tom and Shillingford, Brendan and De Freitas, Nando},
	booktitle={Advances in Neural Information Processing Systems},
	pages={3981--3989},
	year={2016}
}

@article{schmidhuber1995learning,
	title={On learning how to learn learning strategies},
	author={Schmidhuber, Juergen},
	year={1995},
	publisher={Citeseer}
}

@inproceedings{finn2017model,
	title={Model-agnostic meta-learning for fast adaptation of deep networks},
	author={Finn, Chelsea and Abbeel, Pieter and Levine, Sergey},
	booktitle={Proceedings of the 34th International Conference on Machine Learning-Volume 70},
	pages={1126--1135},
	year={2017},
	organization={JMLR. org}
}

@article{liszewski2015speeding,
	title={Speeding Up the Protein Assembly Line},
	author={Liszewski, Kathy},
	journal={Genetic Engineering \& Biotechnology News},
	volume={35},
	number={04},
	pages={1--10},
	year={2015},
	publisher={Mary Ann Liebert, Inc. 140 Huguenot Street, New Rochelle, NY 10801-5215 (914~…}
}

@article{lutz2010beyond,
	title={Beyond directed evolution—semi-rational protein engineering and design},
	author={Lutz, Stefan},
	journal={Current opinion in biotechnology},
	volume={21},
	number={6},
	pages={734--743},
	year={2010},
	publisher={Elsevier}
}

@article{kent2002blat,
	title={BLAT—the BLAST-like alignment tool},
	author={Kent, W James},
	journal={Genome research},
	volume={12},
	number={4},
	pages={656--664},
	year={2002},
	publisher={Cold Spring Harbor Lab}
}

@article{altschul1990basic,
	title={Basic local alignment search tool},
	author={Altschul, Stephen F and Gish, Warren and Miller, Webb and Myers, Eugene W and Lipman, David J},
	journal={Journal of molecular biology},
	volume={215},
	number={3},
	pages={403--410},
	year={1990},
	publisher={Elsevier}
}

@Article{Thompson1997,
	author={Thompson, Julie D.
	and Gibson, Toby J.
	and Plewniak, Fr{\'e}d{\'e}ric
	and Jeanmougin, Fran{\c{c}}ois
	and Higgins, Desmond G.},
	title={The CLUSTAL\_X Windows Interface: Flexible Strategies for Multiple Sequence Alignment Aided by Quality Analysis Tools},
	journal={Nucleic Acids Research},
	year={1997},
	month={Dec},
	day={01},
	volume={25},
	number={24},
	pages={4876-4882},
	abstract={CLUSTAL X is a new windows interface for the widely-used progressive multiple sequence alignment program CLUSTAL W. The new system is easy to use, providing an integrated system for performing multiple sequence and profile alignments and analysing the results. CLUSTAL X displays the sequence alignment in a window on the screen. A versatile sequence colouring scheme allows the user to highlight conserved features in the alignment. Pull-down menus provide all the options required for traditional multiple sequence and profile alignment. New features include: the ability to cut-and-paste sequences to change the order of the alignment, selection of a subset of the sequences to be realigned, and selection of a sub-range of the alignment to be realigned and inserted back into the original alignment. Alignment quality analysis can be performed and low-scoring segments or exceptional residues can be highlighted. Quality analysis and realignment of selected residue ranges provide the user with a powerful tool to improve and refine difficult alignments and to trap errors in input sequences. CLUSTAL X has been compiled on SUN Solaris, IRIX5.3 on Silicon Graphics, Digital UNIX on DECstations, Microsoft Windows (32 bit) for PCs, Linux ELF for x86 PCs, and Macintosh PowerMac.},
	issn={0305-1048},
	doi={10.1093/nar/25.24.4876},
	url={https://doi.org/10.1093/nar/25.24.4876}
}

@article{mst197,
	author = {Tamura, Koichiro and Stecher, Glen and Peterson, Daniel and Filipski, Alan and Kumar, Sudhir},
	title = "{MEGA6: Molecular Evolutionary Genetics Analysis Version 6.0}",
	journal = {Molecular Biology and Evolution},
	volume = {30},
	number = {12},
	pages = {2725-2729},
	year = {2013},
	month = {10},
	abstract = "{We announce the release of an advanced version of the Molecular Evolutionary Genetics Analysis (MEGA) software, which currently contains facilities for building sequence alignments, inferring phylogenetic histories, and conducting molecular evolutionary analysis. In version 6.0, MEGA now enables the inference of timetrees, as it implements the RelTime method for estimating divergence times for all branching points in a phylogeny. A new Timetree Wizard in MEGA6 facilitates this timetree inference by providing a graphical user interface (GUI) to specify the phylogeny and calibration constraints step-by-step. This version also contains enhanced algorithms to search for the optimal trees under evolutionary criteria and implements a more advanced memory management that can double the size of sequence data sets to which MEGA can be applied. Both GUI and command-line versions of MEGA6 can be downloaded from www.megasoftware.net free of charge.}",
	issn = {0737-4038},
	doi = {10.1093/molbev/mst197},
	url = {https://doi.org/10.1093/molbev/mst197},
	eprint = {http://oup.prod.sis.lan/mbe/article-pdf/30/12/2725/19498310/mst197.pdf},
}

@article{boeckmann2003swiss,
	title={The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003},
	author={Boeckmann, Brigitte and Bairoch, Amos and Apweiler, Rolf and Blatter, Marie-Claude and Estreicher, Anne and Gasteiger, Elisabeth and Martin, Maria J and Michoud, Karine and O'donovan, Claire and Phan, Isabelle and others},
	journal={Nucleic acids research},
	volume={31},
	number={1},
	pages={365--370},
	year={2003},
	publisher={Oxford University Press}
}

@Article{Guex1997,
	author={Guex, Nicolas
	and Peitsch, Manuel C.},
	title={SWISS-MODEL and the Swiss-Pdb Viewer: An environment for comparative protein modeling},
	journal={ELECTROPHORESIS},
	year={1997},
	month={Jan},
	day={01},
	publisher={John Wiley \& Sons, Ltd},
	volume={18},
	number={15},
	pages={2714-2723},
	keywords={Protein modeling},
	keywords={Protein structure},
	keywords={Database},
	abstract={Abstract Comparative protein modeling is increasingly gaining interest since it is of great assistance during the rational design of mutagenesis experiments. The availability of this method, and the resulting models, has however been restricted by the availability of expensive computer hardware and software. To overcome these limitations, we have developed an environment for comparative protein modeling that consists of SWISS-MODEL, a server for automated comparative protein modeling and of the SWISS-PdbViewer, a sequence to structure workbench. The Swiss-PdbViewer not only acts as a client for SWISS-MODEL, but also provides a large selection of structure analysis and display tools. In addition, we provide the SWISS-MODEL Repository, a database containing more than 3500 automatically generated protein models. By making such tools freely available to the scientific community, we hope to increase the use of protein structures and models in the process of experiment design.},
	issn={0173-0835},
	doi={10.1002/elps.1150181505},
	url={https://doi.org/10.1002/elps.1150181505}
}

@article{mcguffin2015intfold,
	title={IntFOLD: an integrated server for modelling protein structures and functions from amino acid sequences},
	author={McGuffin, Liam J and Atkins, Jennifer D and Salehe, Bajuna R and Shuid, Ahmad N and Roche, Daniel B},
	journal={Nucleic acids research},
	volume={43},
	number={W1},
	pages={W169--W173},
	year={2015},
	publisher={Oxford University Press}
}

@incollection{leaver2011rosetta3,
	title={ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules},
	author={Leaver-Fay, Andrew and Tyka, Michael and Lewis, Steven M and Lange, Oliver F and Thompson, James and Jacak, Ron and Kaufman, Kristian W and Renfrew, P Douglas and Smith, Colin A and Sheffler, Will and others},
	booktitle={Methods in enzymology},
	volume={487},
	pages={545--574},
	year={2011},
	publisher={Elsevier}
}

@article{eswar2006comparative,
	title={Comparative protein structure modeling using Modeller},
	author={Eswar, Narayanan and Webb, Ben and Marti-Renom, Marc A and Madhusudhan, MS and Eramian, David and Shen, Min-yi and Pieper, Ursula and Sali, Andrej},
	journal={Current protocols in bioinformatics},
	volume={15},
	number={1},
	pages={5--6},
	year={2006},
	publisher={Wiley Online Library}
}

@article{phillips2005scalable,
	title={Scalable molecular dynamics with NAMD},
	author={Phillips, James C and Braun, Rosemary and Wang, Wei and Gumbart, James and Tajkhorshid, Emad and Villa, Elizabeth and Chipot, Christophe and Skeel, Robert D and Kale, Laxmikant and Schulten, Klaus},
	journal={Journal of computational chemistry},
	volume={26},
	number={16},
	pages={1781--1802},
	year={2005},
	publisher={Wiley Online Library}
}

@article{case2005amber,
	title={The Amber biomolecular simulation programs},
	author={Case, David A and Cheatham III, Thomas E and Darden, Tom and Gohlke, Holger and Luo, Ray and Merz Jr, Kenneth M and Onufriev, Alexey and Simmerling, Carlos and Wang, Bing and Woods, Robert J},
	journal={Journal of computational chemistry},
	volume={26},
	number={16},
	pages={1668--1688},
	year={2005},
	publisher={Wiley Online Library}
}

@article{trott2010autodock,
	title={AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading},
	author={Trott, Oleg and Olson, Arthur J},
	journal={Journal of computational chemistry},
	volume={31},
	number={2},
	pages={455--461},
	year={2010},
	publisher={Wiley Online Library}
}

@article{lyskov2008rosettadock,
	title={The RosettaDock server for local protein--protein docking},
	author={Lyskov, Sergey and Gray, Jeffrey J},
	journal={Nucleic acids research},
	volume={36},
	number={suppl\_2},
	pages={W233--W238},
	year={2008},
	publisher={Oxford University Press}
}

@article{tovchigrechko2006gramm,
	title={GRAMM-X public web server for protein--protein docking},
	author={Tovchigrechko, Andrey and Vakser, Ilya A},
	journal={Nucleic acids research},
	volume={34},
	number={suppl\_2},
	pages={W310--W314},
	year={2006},
	publisher={Oxford University Press}
}

@article{release20161,
	title={1: Maestro},
	author={Release, Schr{\"o}dinger},
	journal={Schr{\"o}dinger, LLC, New York, NY, USA},
	year={2016}
}

@article{hua2001novel,
	title={A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach},
	author={Hua, Sujun and Sun, Zhirong},
	journal={Journal of molecular biology},
	volume={308},
	number={2},
	pages={397--407},
	year={2001},
	publisher={Elsevier}
}

@Article{Muggleton1992,
	author={Muggleton, Stephen
	and King, Ross D.
	and Stenberg, Michael J. E.},
	title={Protein secondary structure prediction using logic-based machine learning},
	journal={Protein Engineering, Design and Selection},
	year={1992},
	month={Oct},
	day={01},
	volume={5},
	number={7},
	pages={647-657},
	abstract={Many attempts have been made to solve the problem of predicting protein secondary structure from the primary sequence but the best performance results are still disappointing. In this paper, the use of a machine learning algorithm which allows relational descriptions is shown to lead to improved performance. The Inductive Logic Programming computer program, Golem, was applied to learning secondary structure prediction rules for a/a domain type proteins. The input to the program consisted of 12 non-homologous proteins (1612 residues) of known structure, together with a background knowledge describing the chemical and physical properties of the residues. Golem learned a small set of rules that predict which residues are part of the a-helices--based on their positional relationships and chemical and physical properties. The rules were tested on four independent non-homologous proteins (416 residues) giving an accuracy of 81\% ({\textpm}2\%). This is an improvement, on identical data, over the previously reported result of 73\% by King and Sternberg (1990, J. Mol. Biol., 216, 441-457) using the machine learning program PROMIS, and of 72\% using the standard Gamier-Osguthorpe-Robson method. The best previously reported result in the literature for the a/a domain type is 76\%, achieved using a neural net approach. Machine learning also has the advantage over neural network and statistical methods in producing more understandable results.},
	issn={1741-0126},
	doi={10.1093/protein/5.7.647},
	url={https://doi.org/10.1093/protein/5.7.647}
}

@article{WANG2008262,
	title = "A protein secondary structure prediction framework based on the Extreme Learning Machine",
	journal = "Neurocomputing",
	volume = "72",
	number = "1",
	pages = "262 - 268",
	year = "2008",
	note = "Machine Learning for Signal Processing (MLSP 2006) / Life System Modelling, Simulation, and Bio-inspired Computing (LSMS 2007)",
	issn = "0925-2312",
	doi = "https://doi.org/10.1016/j.neucom.2008.01.016",
	url = "http://www.sciencedirect.com/science/article/pii/S0925231208001094",
	author = "Guoren Wang and Yi Zhao and Di Wang",
	keywords = "ELM, Protein secondary structure prediction, Probability based combination, Helix postprocessing",
	abstract = "In this paper we propose an Extreme Learning Machine (ELM) based protein secondary structure prediction framework which can provide good performance at extremely fast speed. To achieve better performance, in this framework: (i) the three secondary structures are independently predicted by a binary ELM classifier first; (ii) a probability based combination (PBC) method is then proposed to combine these binary prediction results into the expected three-classification results and (iii) a helix postprocessing (HPP) method is finally proposed to further improve the overall performance of the framework based on biological features. Experiments conducted on the real data sets CB513 and RS126 demonstrate that our algorithm can achieve as good prediction accuracy as other popular methods; however, at very fast learning speed."
}

@article{schueler2003conserved,
	title={Conserved residue clustering and protein structure prediction},
	author={Schueler-Furman, Ora and Baker, David},
	journal={Proteins: Structure, Function, and Bioinformatics},
	volume={52},
	number={2},
	pages={225--235},
	year={2003},
	publisher={Wiley Online Library}
}

@article{orengo1997cath,
	title={CATH--a hierarchic classification of protein domain structures},
	author={Orengo, Christine A and Michie, Alex D and Jones, Susan and Jones, David T and Swindells, Mark B and Thornton, Janet M},
	journal={Structure},
	volume={5},
	number={8},
	pages={1093--1109},
	year={1997},
	publisher={Elsevier}
}

@inproceedings{ozbudak2014protein,
	title={Protein Fold Classification using Kohonen's Self-Organizing Map.},
	author={Ozbudak, Ozlem and Dokur, Z{\"u}mray},
	booktitle={IWBBIO},
	pages={903--911},
	year={2014}
}

@article{canutescu2003graph,
	title={A graph-theory algorithm for rapid protein side-chain prediction},
	author={Canutescu, Adrian A and Shelenkov, Andrew A and Dunbrack Jr, Roland L},
	journal={Protein science},
	volume={12},
	number={9},
	pages={2001--2014},
	year={2003},
	publisher={Wiley Online Library}
}

@article{vishveshwara2002protein,
	title={Protein structure: insights from graph theory},
	author={Vishveshwara, Saraswathi and Brinda, KV and Kannan, N},
	journal={Journal of Theoretical and Computational Chemistry},
	volume={1},
	number={01},
	pages={187--211},
	year={2002},
	publisher={World Scientific}
}

@article{martin2017loto,
	title={LoTo: a graphlet based method for the comparison of local topology between gene regulatory networks},
	author={Martin, Alberto J and Contreras-Riquelme, Sebasti{\'a}n and Dominguez, Calixto and Perez-Acle, Tomas},
	journal={PeerJ},
	volume={5},
	pages={e3052},
	year={2017},
	publisher={PeerJ Inc.}
}

@article{jespersen2017bepipred,
	title={BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes},
	author={Jespersen, Martin Closter and Peters, Bjoern and Nielsen, Morten and Marcatili, Paolo},
	journal={Nucleic acids research},
	volume={45},
	number={W1},
	pages={W24--W29},
	year={2017},
	publisher={Oxford University Press}
}

@article{odorico2003bepitope,
	title={BEPITOPE: predicting the location of continuous epitopes and patterns in proteins},
	author={Odorico, Michael and Pellequer, Jean-Luc},
	journal={Journal of Molecular Recognition},
	volume={16},
	number={1},
	pages={20--22},
	year={2003},
	publisher={Wiley Online Library}
}

@misc{saha2008abcpred,
	title={ABCPred benchmarking datasets. 2006a},
	author={Saha, S and Raghava, G},
	year={2008}
}

@article{bedbrook2019machine,
	title={Machine learning-guided channelrhodopsin engineering enables minimally-invasive optogenetics},
	author={Bedbrook, Claire N and Yang, Kevin K and Robinson, J Elliott and Gradinaru, Viviana and Arnold, Frances H},
	year={2019}
}

@article{wu2019machine,
	title={Machine-Learning-Assisted Directed Protein Evolution with Combinatorial Libraries},
	author={Wu, Zachary and Kan, SB and Lewis, Russell D and Wittmann, Bruce J and Arnold, Frances H},
	journal={arXiv preprint arXiv:1902.07231},
	year={2019}
}

@article{yang2018machine,
	title={Machine learning in protein engineering},
	author={Yang, Kevin K and Wu, Zachary and Arnold, Frances H},
	journal={arXiv preprint arXiv:1811.10775},
	year={2018}
}

@article{veljkovic1985possible,
	title={Is it possible to analyze DNA and protein sequences by the methods of digital signal processing?},
	author={Veljkovic, V and Cosic, I and Lalovic, D and others},
	journal={IEEE Transactions on Biomedical Engineering},
	number={5},
	pages={337--341},
	year={1985},
	publisher={IEEE}
}

@article{cosic2016analysis,
	title={Analysis of tumor necrosis factor function using the resonant recognition model},
	author={Cosic, Irena and Cosic, Drasko and Lazar, Katarina},
	journal={Cell biochemistry and biophysics},
	volume={74},
	number={2},
	pages={175--180},
	year={2016},
	publisher={Springer}
}

@article{cadet2018application,
	title={Application of fourier transform and proteochemometrics principles to protein engineering},
	author={Cadet, Fr{\'e}d{\'e}ric and Fontaine, Nicolas and Vetrivel, Iyanar and Chong, Matthieu Ng Fuk and Savriama, Olivier and Cadet, Xavier and Charton, Philippe},
	journal={BMC bioinformatics},
	volume={19},
	number={1},
	pages={382},
	year={2018},
	publisher={BioMed Central}
}

@misc{brownlee2017one,
	title={Why One-Hot Encode Data in Machine Learning},
	author={Brownlee, Jason},
	year={2017}
}

@book{cohen2014applied,
	title={Applied multiple regression/correlation analysis for the behavioral sciences},
	author={Cohen, Patricia and West, Stephen G and Aiken, Leona S},
	year={2014},
	publisher={Psychology Press}
}

@article{peng2005feature,
	title={Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy},
	author={Peng, Hanchuan and Long, Fuhui and Ding, Chris},
	journal={IEEE Transactions on Pattern Analysis \& Machine Intelligence},
	number={8},
	pages={1226--1238},
	year={2005},
	publisher={IEEE}
}

@inproceedings{saeys2008robust,
	title={Robust feature selection using ensemble feature selection techniques},
	author={Saeys, Yvan and Abeel, Thomas and Van de Peer, Yves},
	booktitle={Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
	pages={313--325},
	year={2008},
	organization={Springer}
}

@article{granitto2006recursive,
	title={Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products},
	author={Granitto, Pablo M and Furlanello, Cesare and Biasioli, Franco and Gasperi, Flavia},
	journal={Chemometrics and Intelligent Laboratory Systems},
	volume={83},
	number={2},
	pages={83--90},
	year={2006},
	publisher={Elsevier}
}

@article{hinton2006reducing,
	title={Reducing the dimensionality of data with neural networks},
	author={Hinton, Geoffrey E and Salakhutdinov, Ruslan R},
	journal={science},
	volume={313},
	number={5786},
	pages={504--507},
	year={2006},
	publisher={American Association for the Advancement of Science}
}

@book{jolliffe2011principal,
	title={Principal component analysis},
	author={Jolliffe, Ian},
	year={2011},
	publisher={Springer}
}

@article{wold1987principal,
	title={Principal component analysis},
	author={Wold, Svante and Esbensen, Kim and Geladi, Paul},
	journal={Chemometrics and intelligent laboratory systems},
	volume={2},
	number={1-3},
	pages={37--52},
	year={1987},
	publisher={Elsevier}
}

@inproceedings{artac2002incremental,
	title={Incremental PCA for on-line visual learning and recognition},
	author={Artac, Matej and Jogan, Matjaz and Leonardis, Ales},
	booktitle={Object recognition supported by user interaction for service robots},
	volume={3},
	pages={781--784},
	year={2002},
	organization={IEEE}
}

@article{cosic1994macromolecular,
	title={Macromolecular bioactivity: is it resonant interaction between macromolecules?-Theory and applications},
	author={Cosic, Irena},
	journal={IEEE Transactions on Biomedical Engineering},
	volume={41},
	number={12},
	pages={1101--1114},
	year={1994},
	publisher={IEEE}
}

@article{cosic1987prediction,
	title={Prediction of ‘hot spots’ in SV40 enhancer and relation with experimental data},
	author={{\'C}osi{\'c}, Irena and NESIC, Dobrila},
	journal={European journal of biochemistry},
	volume={170},
	number={1-2},
	pages={247--252},
	year={1987},
	publisher={Wiley Online Library}
}

@article{welch1967use,
	title={The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms},
	author={Welch, Peter},
	journal={IEEE Transactions on audio and electroacoustics},
	volume={15},
	number={2},
	pages={70--73},
	year={1967},
	publisher={IEEE}
}

@article{cooley1970fast,
	title={The fast Fourier transform algorithm: Programming considerations in the calculation of sine, cosine and Laplace transforms},
	author={Cooley, James W and Lewis, PAW and Welch, PD},
	journal={Journal of Sound and Vibration},
	volume={12},
	number={3},
	pages={315--337},
	year={1970},
	publisher={Elsevier}
}

@article{schomburg2004brenda,
	title={BRENDA, the enzyme database: updates and major new developments},
	author={Schomburg, Ida and Chang, Antje and Ebeling, Christian and Gremse, Marion and Heldt, Christian and Huhn, Gregor and Schomburg, Dietmar},
	journal={Nucleic acids research},
	volume={32},
	number={suppl\_1},
	pages={D431--D433},
	year={2004},
	publisher={Oxford University Press}
}

@article{bava2004protherm,
	title={ProTherm, version 4.0: thermodynamic database for proteins and mutants},
	author={Bava, K Abdulla and Gromiha, M Michael and Uedaira, Hatsuho and Kitajima, Koji and Sarai, Akinori},
	journal={Nucleic acids research},
	volume={32},
	number={suppl\_1},
	pages={D120--D121},
	year={2004},
	publisher={Oxford University Press}
}

@book{sneddon1995fourier,
	title={Fourier transforms},
	author={Sneddon, Ian Naismith},
	year={1995},
	publisher={Courier Corporation}
}

@book{rao2014discrete,
	title={Discrete cosine transform: algorithms, advantages, applications},
	author={Rao, K Ramamohan and Yip, Ping},
	year={2014},
	publisher={Academic press}
}

@article{kolba1977prime,
	title={A prime factor FFT algorithm using high-speed convolution},
	author={Kolba, D and Parks, TW},
	journal={IEEE Transactions on Acoustics, Speech, and Signal Processing},
	volume={25},
	number={4},
	pages={281--294},
	year={1977},
	publisher={IEEE}
}

@inproceedings{cui2005some,
	title={Some new parallel fast Fourier transform algorithms},
	author={Cui-xiang, Zhong and Guo-qiang, Han and Ming-He, Huang},
	booktitle={Sixth International Conference on Parallel and Distributed Computing Applications and Technologies (PDCAT'05)},
	pages={624--628},
	year={2005},
	organization={IEEE}
}

@article{muquet2002cyclic,
	title={Cyclic prefixing or zero padding for wireless multicarrier transmissions?},
	author={Muquet, Bertrand and Wang, Zhengdao and Giannakis, Georgios B and De Courville, Marc and Duhamel, Pierre},
	journal={IEEE Transactions on communications},
	volume={50},
	number={12},
	pages={2136--2148},
	year={2002},
	publisher={IEEE}
}

@article{jain1999data,
	title={Data clustering: a review},
	author={Jain, Anil K and Murty, M Narasimha and Flynn, Patrick J},
	journal={ACM computing surveys (CSUR)},
	volume={31},
	number={3},
	pages={264--323},
	year={1999},
	publisher={Acm}
}

@inproceedings{arthur2007k,
	title={k-means++: The advantages of careful seeding},
	author={Arthur, David and Vassilvitskii, Sergei},
	booktitle={Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms},
	pages={1027--1035},
	year={2007},
	organization={Society for Industrial and Applied Mathematics}
}

@article{frey2007clustering,
	title={Clustering by passing messages between data points},
	author={Frey, Brendan J and Dueck, Delbert},
	journal={science},
	volume={315},
	number={5814},
	pages={972--976},
	year={2007},
	publisher={American Association for the Advancement of Science}
}

@article{johnson1967hierarchical,
	title={Hierarchical clustering schemes},
	author={Johnson, Stephen C},
	journal={Psychometrika},
	volume={32},
	number={3},
	pages={241--254},
	year={1967},
	publisher={Springer}
}

@article{sokal1962comparison,
	title={The comparison of dendrograms by objective methods},
	author={Sokal, Robert R and Rohlf, F James},
	journal={Taxon},
	volume={11},
	number={2},
	pages={33--40},
	year={1962},
	publisher={JSTOR}
}

@article{cheng1995mean,
	title={Mean shift, mode seeking, and clustering},
	author={Cheng, Yizong},
	journal={IEEE transactions on pattern analysis and machine intelligence},
	volume={17},
	number={8},
	pages={790--799},
	year={1995},
	publisher={IEEE}
}

@article{tran2013revised,
	title={Revised DBSCAN algorithm to cluster data with dense adjacent clusters},
	author={Tran, Thanh N and Drab, Klaudia and Daszykowski, Michal},
	journal={Chemometrics and Intelligent Laboratory Systems},
	volume={120},
	pages={92--96},
	year={2013},
	publisher={Elsevier}
}

@article{zhang1997birch,
	title={BIRCH: A new data clustering algorithm and its applications},
	author={Zhang, Tian and Ramakrishnan, Raghu and Livny, Miron},
	journal={Data Mining and Knowledge Discovery},
	volume={1},
	number={2},
	pages={141--182},
	year={1997},
	publisher={Springer}
}

@inproceedings{zivkovic2004improved,
	title={Improved adaptive Gaussian mixture model for background subtraction.},
	author={Zivkovic, Zoran and others},
	booktitle={ICPR (2)},
	pages={28--31},
	year={2004}
}

@article{moon1996expectation,
	title={The expectation-maximization algorithm},
	author={Moon, Todd K},
	journal={IEEE Signal processing magazine},
	volume={13},
	number={6},
	pages={47--60},
	year={1996},
	publisher={IEEE}
}

@article{yeung2001details,
	title={Details of the adjusted rand index and clustering algorithms, supplement to the paper an empirical study on principal component analysis for clustering gene expression data},
	author={Yeung, Ka Yee and Ruzzo, Walter L},
	journal={Bioinformatics},
	volume={17},
	number={9},
	pages={763--774},
	year={2001}
}

@inproceedings{rosenberg2007v,
	title={V-measure: A conditional entropy-based external cluster evaluation measure},
	author={Rosenberg, Andrew and Hirschberg, Julia},
	booktitle={Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL)},
	pages={410--420},
	year={2007}
}

@article{rousseeuw1987silhouettes,
	title={Silhouettes: a graphical aid to the interpretation and validation of cluster analysis},
	author={Rousseeuw, Peter J},
	journal={Journal of computational and applied mathematics},
	volume={20},
	pages={53--65},
	year={1987},
	publisher={Elsevier}
}

@article{calinski1974dendrite,
	title={A dendrite method for cluster analysis},
	author={Cali{\'n}ski, Tadeusz and Harabasz, Jerzy},
	journal={Communications in Statistics-theory and Methods},
	volume={3},
	number={1},
	pages={1--27},
	year={1974},
	publisher={Taylor \& Francis}
}

@article{fortunato2012community,
	title={Community structure in graphs},
	author={Fortunato, Santo and Castellano, Claudio},
	journal={Computational Complexity: Theory, Techniques, and Applications},
	pages={490--512},
	year={2012},
	publisher={Springer}
}

@article{newman2004detecting,
	title={Detecting community structure in networks},
	author={Newman, Mark EJ},
	journal={The European Physical Journal B},
	volume={38},
	number={2},
	pages={321--330},
	year={2004},
	publisher={Springer}
}

@article {Newman8577,
	author = {Newman, M. E. J.},
	title = {Modularity and community structure in networks},
	volume = {103},
	number = {23},
	pages = {8577--8582},
	year = {2006},
	doi = {10.1073/pnas.0601602103},
	publisher = {National Academy of Sciences},
	abstract = {Many networks of interest in the sciences, including social networks, computer networks, and metabolic and regulatory networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure is one of the outstanding issues in the study of networked systems. One highly effective approach is the optimization of the quality function known as {\textquotedblleft}modularity{\textquotedblright} over the possible divisions of a network. Here I show that the modularity can be expressed in terms of the eigenvectors of a characteristic matrix for the network, which I call the modularity matrix, and that this expression leads to a spectral algorithm for community detection that returns results of demonstrably higher quality than competing methods in shorter running times. I illustrate the method with applications to several published network data sets.},
	issn = {0027-8424},
	URL = {https://www.pnas.org/content/103/23/8577},
	eprint = {https://www.pnas.org/content/103/23/8577.full.pdf},
	journal = {Proceedings of the National Academy of Sciences}
}

license: MIT

license_type: permissive

branch_name: refs/heads/master

extension: bib


text:
@manual{anleitung01,
  author = "TU-Dortmund",
  title = "Versuch V355: Gekoppelte Schwingkreise",
  date = "2017-01-15",
  url = "http://129.217.224.2/HOMEPAGE/PHYSIKER/BACHELOR/AP/SKRIPT/V355.pdf",
}


text:
$("#enableRedirects").click((e)=>{
    $("#enableStrictMode").prop('checked',false);
    chrome.storage.sync.set({
        redirectEnabled: e.currentTarget.checked,
        strictModeEnabled: false
      });
})

$("#enableStrictMode").click((e)=>{
    $("#enableRedirects").prop('checked',false);
    chrome.storage.sync.set({
        redirectEnabled: false,
        strictModeEnabled: e.currentTarget.checked
      });
})

document.addEventListener('DOMContentLoaded',()=>{
    chrome.storage.sync.get({
        redirectEnabled: false,
        strictModeEnabled: false
      }, function(items) {
        $("#enableRedirects").prop('checked', items.redirectEnabled);
        $("#enableStrictMode").prop('checked', items.strictModeEnabled);
      });
});

repository: sarthology/nomoogle

repo_name: sarthology/nomoogle

gha_license_id: MIT

gha_language: JavaScript

extension: js


text:
package env

import (
	"encoding/json"
	"fmt"
	"io/ioutil"
	"os"
	"path/filepath"
	"runtime"

	"github.com/joho/godotenv"
)

// APIMode gin runtime mode (debug|release|test)
var APIMode string

// AWSAuthRegion availability region for amazon cognito
var AWSAuthRegion string

// AWSAuthKey api key for amazon cognito
var AWSAuthKey string

// AWSAuthID amazon id for cognito
var AWSAuthID string

// CognitoClientID cognito client id
var CognitoClientID string

// KafkaBroker kafka host for connection
var KafkaBroker string

// APIPort serving port
var APIPort string

// IpRange ip ranges for users without basic auth
var IpRange string

// Access control model
var AccessModelPath string

// Access control policy
var AccessPolicyPath string

// RedisAddr url for cache connection
var RedisAddr string

// RedisPassword for cache authentication
var RedisPassword string

// KafkaCreds kafka credentials
var KafkaCreds struct {
	Username string `json:"username"`
	Password string `json:"password"`
}

const kafkaBroker = "KAFKA_BROKER"
const apiMode = "API_MODE"
const apiPort = "API_PORT"
const kafkaCreds = "KAFKA_CREDS"
const redisAddr = "REDIS_ADDR"
const redisPassword = "REDIS_PASSWORD"

const awsAuthRegion = "AWS_AUTH_REGION"
const awsAuthKey = "AWS_AUTH_KEY"
const awsAuthID = "AWS_AUTH_ID"
const cognitoClientID = "COGNITO_CLIENT_ID"

const ipRange = "IP_RANGE"
const accessModel = "ACCESS_MODEL"
const accessPolicy = "ACCESS_POLICY"

var variables = map[*string]string{
	&KafkaBroker:     kafkaBroker,
	&APIMode:         apiMode,
	&APIPort:         apiPort,
	&AWSAuthRegion:   awsAuthRegion,
	&AWSAuthKey:      awsAuthKey,
	&AWSAuthID:       awsAuthID,
	&CognitoClientID: cognitoClientID,
	&IpRange:         ipRange,
	&RedisAddr:       redisAddr,
	&RedisPassword:   redisPassword,
}

type fileVar struct {
	name string
	path string
	file *string
}

var files = []*fileVar{
	{
		name: accessModel,
		path: "./model.conf",
		file: &AccessModelPath,
	},
	{
		name: accessPolicy,
		path: "./policy.csv",
		file: &AccessPolicyPath,
	},
}

const errorMessage = "env variable '%s' not found"

// Init environment params
func Init() error {
	var (
		_, b, _, _ = runtime.Caller(0)
		base       = filepath.Dir(b)
		exists     bool
		_          = godotenv.Load(fmt.Sprintf("%s/../../.env", base))
	)

	for ref, name := range variables {
		*ref, exists = os.LookupEnv(name)
		if !exists {
			return fmt.Errorf(errorMessage, name)
		}
	}

	creds, exists := os.LookupEnv(kafkaCreds)

	if exists {
		if err := json.Unmarshal([]byte(creds), &KafkaCreds); err != nil {
			return fmt.Errorf("can't unmarshal kafka credentials: %w", err)
		}
	}

	for _, fv := range files {
		var err error
		content, exists := os.LookupEnv(fv.name)

		if !exists {
			*fv.file = fv.path
			continue
		}

		filename := filepath.Base(fv.path)
		f, err := ioutil.TempFile("/tmp", filename)

		if err != nil {
			return fmt.Errorf("error creating tmp file %s: %w", filename, err)
		}

		_, err = f.Write([]byte(content))

		if err != nil {
			return fmt.Errorf("error writing to tmp file %s: %w", filename, err)
		}

		*fv.file = f.Name()
	}

	return nil
}

repository: wikimedia/OKAPI

repo_name: wikimedia/OKAPI

extension: go


text:
require recipes-devtools/rust/rust-source-${PV}.inc
require recipes-devtools/rust/rust-snapshot-${PV}.inc
require cargo.inc

LIC_FILES_CHKSUM += " \
    file://LICENSE-APACHE;md5=71b224ca933f0676e26d5c2e2271331c \
    file://LICENSE-THIRD-PARTY;md5=f257ad009884cb88a3a87d6920e7180a \
"

repository: anguslees/meta-rust

repo_name: anguslees/meta-rust

extension: bb