id string | sources list | title string | abstract string | authors list | categories list | fields_of_study list | published_date timestamp[s] | url string | pdf_url string | arxiv_id string | doi string | citation_count int64 | influential_citation_count int64 | has_code bool | code_url string | venue string | quality_score float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
b4f1f40ac7272d1fd17b698fe4612591ab417a616b32ec1851b903e1c36dd0ea | [
"arxiv",
"semantic_scholar"
] | DS-ProGen: A Dual-Structure Deep Language Model for Functional Protein Design | Inverse Protein Folding (IPF) is a critical subtask in the field of protein design, aiming to engineer amino acid sequences capable of folding correctly into a specified three-dimensional (3D) conformation. Although substantial progress has been achieved in recent years, existing methods generally rely on either backbo... | [
"Yanting Li",
"Jiyue Jiang",
"Zikang Wang",
"Ziqian Lin",
"Dongchen He",
"Yuheng Shan",
"Yanruisheng Shao",
"Jiayi Li",
"Xiangyu Shi",
"Jiuming Wang",
"Yanyu Chen",
"Yimin Fan",
"Han Li",
"Yu Li"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-05-18T00:00:00 | https://arxiv.org/abs/2505.12511 | https://arxiv.org/pdf/2505.12511v1 | 2505.12511 | 10.48550/arXiv.2505.12511 | 4 | 0 | false | null | arXiv.org | 0.1747 |
837c900713503c342909a2496f3460510a79040724d12115c9b6b73d0b594224 | [
"arxiv",
"semantic_scholar"
] | mCLM: A Modular Chemical Language Model that Generates Functional and Makeable Molecules | Despite their ability to understand chemical knowledge, large language models (LLMs) remain limited in their capacity to propose novel molecules with desired functions (e.g., drug-like properties). In addition, the molecules that LLMs propose can often be challenging to make, and are almost never compatible with automa... | [
"Carl Edwards",
"Chi Han",
"Gawon Lee",
"Thao Nguyen",
"Sara Szymkuć",
"Chetan Kumar Prasad",
"Bowen Jin",
"Jiawei Han",
"Ying Diao",
"Ge Liu",
"Hao Peng",
"Bartosz A. Grzybowski",
"Martin D. Burke",
"Heng Ji"
] | [
"cs.AI",
"cs.CL",
"cs.LG",
"q-bio.QM"
] | [
"Computer Science",
"Biology"
] | 2025-05-18T00:00:00 | https://arxiv.org/abs/2505.12565 | https://arxiv.org/pdf/2505.12565v3 | 2505.12565 | null | 3 | 0 | true | https://github.com/blender-nlp/mCLM | null | 0.1936 |
7b2dd90cd28ccae9661abea23df5c16eede0bda264c9f056c1b7bd2df7ae5146 | [
"arxiv",
"semantic_scholar"
] | Retrospex: Language Agent Meets Offline Reinforcement Learning Critic | Large Language Models (LLMs) possess extensive knowledge and commonsense reasoning capabilities, making them valuable for creating powerful agents. However, existing LLM agent frameworks have not fully utilized past experiences for improvement. This work introduces a new LLM-based agent framework called Retrospex, whic... | [
"Yufei Xiang",
"Yiqun Shen",
"Yeqin Zhang",
"Cam-Tu Nguyen"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-05-17T00:00:00 | https://arxiv.org/abs/2505.11807 | https://arxiv.org/pdf/2505.11807v2 | 2505.11807 | 10.18653/v1/2024.emnlp-main.268 | 10 | 1 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.2603 |
e1b1e22412cee9a8413633521fbc8924a00879cae51fd11404dee29ec89e416c | [
"arxiv",
"semantic_scholar"
] | Prot2Text-V2: Protein Function Prediction with Multimodal Contrastive Alignment | Predicting protein function from sequence is a central challenge in computational biology. While existing methods rely heavily on structured ontologies or similarity-based techniques, they often lack the flexibility to express structure-free functional descriptions and novel biological functions. In this work, we intro... | [
"Xiao Fei",
"Michail Chatzianastasis",
"Sarah Almeida Carneiro",
"Hadi Abdine",
"Lawrence P. Petalidis",
"Michalis Vazirgiannis"
] | [
"cs.CE"
] | [
"Computer Science"
] | 2025-05-16T00:00:00 | https://arxiv.org/abs/2505.11194 | https://arxiv.org/pdf/2505.11194v3 | 2505.11194 | 10.48550/arXiv.2505.11194 | 9 | 1 | false | null | arXiv.org | 0.25 |
27c49389010ae89a0d9358f1a30f7dee6c74f0893247838e9b585bbe6ce043c9 | [
"arxiv",
"semantic_scholar"
] | PSBench: a large-scale benchmark for estimating the accuracy of protein complex structural models | Predicting protein complex structures is essential for protein function analysis, protein design, and drug discovery. While AI methods like AlphaFold can predict accurate structural models for many protein complexes, reliably estimating the quality of these predicted models (estimation of model accuracy, or EMA) for mo... | [
"Pawan Neupane",
"Jian Liu",
"Jianlin Cheng"
] | [
"q-bio.BM",
"cs.AI",
"cs.LG"
] | [
"Biology",
"Computer Science"
] | 2025-05-13T00:00:00 | https://arxiv.org/abs/2505.22674 | https://arxiv.org/pdf/2505.22674v1 | 2505.22674 | 10.48550/arXiv.2505.22674 | 1 | 0 | true | https://github.com/BioinfoMachineLearning/PSBench | arXiv.org | 0.2444 |
2cdebf922251b59288caffdb14c04ce3766d093edb67b5d8c93dd5f967c3e151 | [
"arxiv",
"semantic_scholar"
] | Protein FID: Improved Evaluation of Protein Structure Generative Models | Protein structure generative models have seen a recent surge of interest, but meaningfully evaluating them computationally is an active area of research. While current metrics have driven useful progress, they do not capture how well models sample the design space represented by the training data. We argue for a protei... | [
"Felix Faltings",
"Hannes Stark",
"Tommi Jaakkola",
"Regina Barzilay"
] | [
"q-bio.BM"
] | [
"Biology",
"Computer Science",
"Medicine"
] | 2025-05-12T00:00:00 | https://arxiv.org/abs/2505.08041 | https://arxiv.org/pdf/2505.08041v3 | 2505.08041 | 10.1093/bioinformatics/btag156 | 4 | 1 | false | null | null | 0.1747 |
636824137d738d7ae15462c9e0dd66a83ebc33e7bfefa785d71b8644117cfd5f | [
"arxiv",
"semantic_scholar"
] | LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization | Recent advances in Protein Structure Prediction Models (PPMs), such as AlphaFold2 and ESMFold, have revolutionized computational biology by achieving unprecedented accuracy in predicting three-dimensional protein folding structures. However, these models face significant scalability challenges, particularly when proces... | [
"Seunghee Han",
"Soongyu Choi",
"Joo-Young Kim"
] | [
"cs.AR",
"cs.AI",
"cs.ET",
"cs.LG",
"q-bio.BM"
] | [
"Computer Science",
"Biology"
] | 2025-05-09T00:00:00 | https://arxiv.org/abs/2505.05893 | https://arxiv.org/pdf/2505.05893v1 | 2505.05893 | 10.1145/3695053.3731006 | 1 | 0 | false | null | International Symposium on Computer Architecture | 0.1535 |
963314eb269d2e2345cc092199d74b33dedf6d3c4c887720ce0cda7a84e163de | [
"arxiv",
"semantic_scholar"
] | Exploring zero-shot structure-based protein fitness prediction | The ability to make zero-shot predictions about the fitness consequences of protein sequence changes with pre-trained machine learning models enables many practical applications. Such models can be applied for downstream tasks like genetic variant interpretation and protein engineering without additional labeled data. ... | [
"Arnav Sharma",
"Anthony Gitter"
] | [
"q-bio.QM",
"cs.LG",
"q-bio.BM"
] | [
"Computer Science",
"Biology"
] | 2025-04-23T00:00:00 | https://arxiv.org/abs/2504.16886 | https://arxiv.org/pdf/2504.16886v1 | 2504.16886 | 10.48550/arXiv.2504.16886 | 4 | 0 | false | null | arXiv.org | 0.1747 |
afb2322ea7aa59d0c4f288dfa2b5fb493ab895304f81da54185830317f4f52af | [
"arxiv",
"semantic_scholar"
] | Enhancing TCR-Peptide Interaction Prediction with Pretrained Language Models and Molecular Representations | Understanding the binding specificity between T-cell receptors (TCRs) and peptide-major histocompatibility complexes (pMHCs) is central to immunotherapy and vaccine development. However, current predictive models struggle with generalization, especially in data-scarce settings and when faced with novel epitopes. We pre... | [
"Cong Qi",
"Hanzhang Fang",
"Siqi jiang",
"Tianxing Hu",
"Zhi Wei"
] | [
"q-bio.QM",
"cs.CL",
"cs.LG"
] | [
"Computer Science",
"Biology"
] | 2025-04-22T00:00:00 | https://arxiv.org/abs/2505.01433 | https://arxiv.org/pdf/2505.01433v2 | 2505.01433 | 10.48550/arXiv.2505.01433 | 4 | 0 | false | null | arXiv.org | 0.1747 |
75270270ab32be2b14b2ff55d3516cf071c37a467a2ab403066bf3438e31726f | [
"arxiv",
"semantic_scholar"
] | Hierarchical protein backbone generation with latent and structure diffusion | We propose a hierarchical protein backbone generative model that separates coarse and fine-grained details. Our approach called LSD consists of two stages: sampling latents which are decoded into a contact map then sampling atomic coordinates conditioned on the contact map. LSD allows new ways to control protein genera... | [
"Jason Yim",
"Marouane Jaakik",
"Ge Liu",
"Jacob Gershon",
"Karsten Kreis",
"David Baker",
"Regina Barzilay",
"Tommi Jaakkola"
] | [
"q-bio.QM"
] | [
"Biology"
] | 2025-04-12T00:00:00 | https://arxiv.org/abs/2504.09374 | https://arxiv.org/pdf/2504.09374v1 | 2504.09374 | null | 4 | 0 | false | null | null | 0.1747 |
8dc4b056fbf6a1bbda231b1dc003278466b3ab62865895f7e996e7f222d9786d | [
"arxiv",
"semantic_scholar"
] | Mass Balance Approximation of Unfolding Improves Potential-Like Methods for Protein Stability Predictions | The prediction of protein stability changes following single-point mutations plays a pivotal role in computational biology, particularly in areas like drug discovery, enzyme reengineering, and genetic disease analysis. Although deep-learning strategies have pushed the field forward, their use in standard workflows rema... | [
"Ivan Rossi",
"Guido Barducci",
"Tiziana Sanavia",
"Paola Turina",
"Emidio Capriotti",
"Piero Fariselli"
] | [
"q-bio.QM",
"cs.LG",
"physics.bio-ph"
] | [
"Biology",
"Computer Science",
"Physics",
"Medicine"
] | 2025-04-09T00:00:00 | https://arxiv.org/abs/2504.06806 | https://arxiv.org/pdf/2504.06806v1 | 2504.06806 | 10.1002/pro.70134 | 1 | 0 | false | null | Protein Science | 0.1192 |
cf49baa7e01ca56e39ecab9e8c4683189ad5647e6b551ac4b3e5f51fa7481ced | [
"arxiv",
"semantic_scholar"
] | Prot42: a Novel Family of Protein Language Models for Target-aware Protein Binder Generation | Unlocking the next generation of biotechnology and therapeutic innovation demands overcoming the inherent complexity and resource-intensity of conventional protein engineering methods. Recent GenAI-powered computational techniques often rely on the availability of the target protein's 3D structures and specific binding... | [
"Mohammad Amaan Sayeed",
"Engin Tekin",
"Maryam Nadeem",
"Nancy A. ElNaker",
"Aahan Singh",
"Natalia Vassilieva",
"Boulbaba Ben Amor"
] | [
"q-bio.BM",
"cs.AI",
"cs.CL",
"cs.LG"
] | [
"Computer Science",
"Biology"
] | 2025-04-06T00:00:00 | https://arxiv.org/abs/2504.04453 | https://arxiv.org/pdf/2504.04453v2 | 2504.04453 | 10.48550/arXiv.2504.04453 | 4 | 0 | false | null | arXiv.org | 0.1747 |
a566ce5dae25c97202c197812ccae9b125ad068153ce9c3796f70307a2cb94c9 | [
"arxiv",
"semantic_scholar"
] | Redefining technology for indigenous languages | In this paper, we offer an overview of indigenous languages, identifying the causes of their devaluation and the need for legislation on language rights. We review the technologies used to revitalize these languages, finding that when they come from outside, they often have the opposite effect to what they seek; howeve... | [
"Silvia Fernandez-Sabido",
"Laura Peniche-Sabido"
] | [
"cs.CY",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2025-04-02T00:00:00 | https://arxiv.org/abs/2504.01522 | https://arxiv.org/pdf/2504.01522v1 | 2504.01522 | 10.48550/arXiv.2504.01522 | 2 | 0 | false | null | arXiv.org | 0.1193 |
03f8a851f506dedca933fb4e1b20645ea38a071300e5ac23e7c47c55efd07ff2 | [
"arxiv",
"semantic_scholar"
] | Deep Learning-Driven Protein Structure Prediction and Design: Key Model Developments by Nobel Laureates and Multi-Domain Applications | This systematic review outlines pivotal advancements in deep learning-driven protein structure prediction and design, focusing on four core models-AlphaFold, RoseTTAFold, RFDiffusion, and ProteinMPNN-developed by 2024 Nobel Laureates in Chemistry: David Baker, Demis Hassabis, and John Jumper. We analyze their technolog... | [
"Wanqing Yang",
"Yanwei Wang",
"Yang Wang"
] | [
"physics.bio-ph"
] | [
"Physics"
] | 2025-04-02T00:00:00 | https://arxiv.org/abs/2504.01490 | https://arxiv.org/pdf/2504.01490v1 | 2504.01490 | 10.1063/5.0273394 | 3 | 0 | false | null | Biophysical Reviews | 0.1505 |
d9dda0d443c7e8c9b1fd997dd66d9fc005c470f956501cccbc1ab88d0c3ff2ff | [
"arxiv",
"semantic_scholar"
] | Whisper-LM: Improving ASR Models with Language Models for Low-Resource Languages | Automatic speech recognition systems have undoubtedly advanced with the integration of multilingual and multitask models such as Whisper, which have shown a promising ability to understand and process speech across a wide range of languages. Despite their robustness, these models often fall short in handling the lingui... | [
"Xabier de Zuazo",
"Eva Navas",
"Ibon Saratxaga",
"Inma Hernáez Rioja"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-03-30T00:00:00 | https://arxiv.org/abs/2503.23542 | https://arxiv.org/pdf/2503.23542v1 | 2503.23542 | 10.48550/arXiv.2503.23542 | 7 | 1 | true | null | arXiv.org | 0.2258 |
2c472c1f645ae9baaf258fcc8b6eb9a6704e65ff6d17c8d26451f35fa9426f53 | [
"arxiv",
"semantic_scholar"
] | MSNGO: multi-species protein function annotation based on 3D protein structure and network propagation | Motivation: In recent years, protein function prediction has broken through the bottleneck of sequence features, significantly improving prediction accuracy using high-precision protein structures predicted by AlphaFold2. While single-species protein function prediction methods have achieved remarkable success, multi-s... | [
"Beibei Wang",
"Boyue Cui",
"Shiqu Chen",
"Xuan Wang",
"Yadong Wang",
"Junyi Li"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science",
"Medicine"
] | 2025-03-29T00:00:00 | https://arxiv.org/abs/2503.23014 | https://arxiv.org/pdf/2503.23014v1 | 2503.23014 | 10.1093/bioinformatics/btaf285 | 1 | 0 | true | https://github.com/blingbell/MSNGO | null | 0.1259 |
821ee1cc8c1434c0619135bd61b4214eefca6e79fc953982a09c82e1b173ebe5 | [
"arxiv",
"semantic_scholar"
] | How do language models learn facts? Dynamics, curricula and hallucinations | Large language models accumulate vast knowledge during pre-training, yet the dynamics governing this acquisition remain poorly understood. This work investigates the learning dynamics of language models on a synthetic factual recall task, uncovering three key findings: First, language models learn in three phases, exhi... | [
"Nicolas Zucchet",
"Jörg Bornschein",
"Stephanie Chan",
"Andrew Lampinen",
"Razvan Pascanu",
"Soham De"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2025-03-27T00:00:00 | https://arxiv.org/abs/2503.21676 | https://arxiv.org/pdf/2503.21676v2 | 2503.21676 | 10.48550/arXiv.2503.21676 | 28 | 2 | false | null | arXiv.org | 0.3656 |
46e883a42cd3cdb7d791ac0bd1deebc82bf982ca0ed7d7c85179075a058e2e8f | [
"arxiv",
"semantic_scholar"
] | Untangling the Influence of Typology, Data and Model Architecture on Ranking Transfer Languages for Cross-Lingual POS Tagging | Cross-lingual transfer learning is an invaluable tool for overcoming data scarcity, yet selecting a suitable transfer language remains a challenge. The precise roles of linguistic typology, training data, and model architecture in transfer language choice are not fully understood. We take a holistic approach, examining... | [
"Enora Rice",
"Ali Marashian",
"Hannah Haynie",
"Katharina von der Wense",
"Alexis Palmer"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-03-25T00:00:00 | https://arxiv.org/abs/2503.19979 | https://arxiv.org/pdf/2503.19979v1 | 2503.19979 | 10.48550/arXiv.2503.19979 | 7 | 0 | false | null | null | 0.2258 |
7b708d8632039afac42213ec7243fceb22274a35056a7e71111d31960962e839 | [
"arxiv",
"semantic_scholar"
] | An Energy-Adaptive Elastic Equivariant Transformer Framework for Protein Structure Representation | Structure-informed protein representation learning is essential for effective protein function annotation and \textit{de novo} design. However, the presence of inherent noise in both crystal and AlphaFold-predicted structures poses significant challenges for existing methods in learning robust protein representations. ... | [
"Zhongyue Zhang",
"Runze Ma",
"Yanjie Huang",
"Shuangjia Zheng"
] | [
"q-bio.BM"
] | [
"Biology"
] | 2025-03-21T00:00:00 | https://arxiv.org/abs/2503.16996 | https://arxiv.org/pdf/2503.16996v2 | 2503.16996 | null | 0 | 0 | false | null | null | 0.062 |
59f4bbaec903b04997379e7c6962630592cadb7609ec8f637c98c9cdca814ba2 | [
"arxiv",
"semantic_scholar"
] | VenusFactory: A Unified Platform for Protein Engineering Data Retrieval and Language Model Fine-Tuning | Natural language processing (NLP) has significantly influenced scientific domains beyond human language, including protein engineering, where pre-trained protein language models (PLMs) have demonstrated remarkable success. However, interdisciplinary adoption remains limited due to challenges in data collection, task be... | [
"Yang Tan",
"Chen Liu",
"Jingyuan Gao",
"Banghao Wu",
"Mingchen Li",
"Ruilin Wang",
"Lingrong Zhang",
"Huiqun Yu",
"Guisheng Fan",
"Liang Hong",
"Bingxin Zhou"
] | [
"cs.CL",
"cs.AI",
"q-bio.QM"
] | [
"Computer Science",
"Biology"
] | 2025-03-19T00:00:00 | https://arxiv.org/abs/2503.15438 | https://arxiv.org/pdf/2503.15438v1 | 2503.15438 | 10.48550/arXiv.2503.15438 | 6 | 1 | true | https://github.com/tyang816/VenusFactory | arXiv.org | 0.2113 |
3ae3f6a8ba9678a64763fe7a464165efc46ea6963da15dad7541f6b2b6f2c495 | [
"arxiv",
"semantic_scholar"
] | Advanced Deep Learning Methods for Protein Structure Prediction and Design | After AlphaFold won the Nobel Prize, protein prediction with deep learning once again became a hot topic. We comprehensively explore advanced deep learning methods applied to protein structure prediction and design. It begins by examining recent innovations in prediction architectures, with detailed discussions on impr... | [
"Yichao Zhang",
"Ningyuan Deng",
"Xinyuan Song",
"Ziqian Bi",
"Tianyang Wang",
"Zheyu Yao",
"Keyu Chen",
"Ming Li",
"Qian Niu",
"Junyu Liu",
"Benji Peng",
"Sen Zhang",
"Ming Liu",
"Li Zhang",
"Xuanhe Pan",
"Jinlang Wang",
"Pohsun Feng",
"Yizhu Wen",
"Lawrence KQ Yan",
"Hongming... | [
"q-bio.BM",
"cs.AI",
"cs.LG"
] | [
"Computer Science",
"Biology"
] | 2025-03-14T00:00:00 | https://arxiv.org/abs/2503.13522 | https://arxiv.org/pdf/2503.13522v3 | 2503.13522 | 10.48550/arXiv.2503.13522 | 2 | 0 | true | null | arXiv.org | 0.1381 |
19c8074f67488059ca22bb2e779f4ef7effcc8b0d3b7258dcbbd97f6e94c326e | [
"arxiv",
"semantic_scholar"
] | Towards Interpretable Protein Structure Prediction with Sparse Autoencoders | Protein language models have revolutionized structure prediction, but their nonlinear nature obscures how sequence representations inform structure prediction. While sparse autoencoders (SAEs) offer a path to interpretability here by learning linear representations in high-dimensional space, their application has been ... | [
"Nithin Parsan",
"David J. Yang",
"John J. Yang"
] | [
"q-bio.BM",
"cs.AI",
"cs.LG"
] | [
"Biology",
"Computer Science"
] | 2025-03-11T00:00:00 | https://arxiv.org/abs/2503.08764 | https://arxiv.org/pdf/2503.08764v1 | 2503.08764 | 10.48550/arXiv.2503.08764 | 15 | 1 | true | https://github.com/johnyang101/reticular-sae | arXiv.org | 0.301 |
3d3148436e5150410021c3e184caafa9fefe69fe0c965813f99217391bf5e7ed | [
"arxiv",
"semantic_scholar"
] | ProtTeX: Structure-In-Context Reasoning and Editing of Proteins with Large Language Models | Large language models have made remarkable progress in the field of molecular science, particularly in understanding and generating functional small molecules. This success is largely attributed to the effectiveness of molecular tokenization strategies. In protein science, the amino acid sequence serves as the sole tok... | [
"Zicheng Ma",
"Chuanliu Fan",
"Zhicong Wang",
"Zhenyu Chen",
"Xiaohan Lin",
"Yanheng Li",
"Shihao Feng",
"Jun Zhang",
"Ziqiang Cao",
"Yi Qin Gao"
] | [
"q-bio.BM",
"cs.AI"
] | [
"Biology",
"Computer Science",
"Medicine"
] | 2025-03-11T00:00:00 | https://arxiv.org/abs/2503.08179 | https://arxiv.org/pdf/2503.08179v3 | 2503.08179 | 10.48550/arXiv.2503.08179 | 10 | 1 | false | null | Journal of Chemical Information and Modeling | 0.2603 |
af6b3da96db957f1cc1265a17d0210b40420b126272cf9bc88edbdf32599fd9f | [
"arxiv",
"semantic_scholar"
] | Gender Encoding Patterns in Pretrained Language Model Representations | Gender bias in pretrained language models (PLMs) poses significant social and ethical challenges. Despite growing awareness, there is a lack of comprehensive investigation into how different models internally represent and propagate such biases. This study adopts an information-theoretic approach to analyze how gender ... | [
"Mahdi Zakizadeh",
"Mohammad Taher Pilehvar"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-03-09T00:00:00 | https://arxiv.org/abs/2503.06734 | https://arxiv.org/pdf/2503.06734v1 | 2503.06734 | 10.48550/arXiv.2503.06734 | 2 | 0 | false | null | null | 0.1193 |
48d57fb56749f8d7d348ccefc4445bf2303b6182678776f09bd86f4951deaa38 | [
"arxiv",
"semantic_scholar"
] | From Language to Cognition: How LLMs Outgrow the Human Language Network | Large language models (LLMs) exhibit remarkable similarity to neural activity in the human language network. However, the key properties of language shaping brain-like representations, and their evolution during training as a function of different tasks remain unclear. We here benchmark 34 training checkpoints spanning... | [
"Badr AlKhamissi",
"Greta Tuckute",
"Yingtian Tang",
"Taha Binhuraib",
"Antoine Bosselut",
"Martin Schrimpf"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-03-03T00:00:00 | https://arxiv.org/abs/2503.01830 | https://arxiv.org/pdf/2503.01830v2 | 2503.01830 | 10.48550/arXiv.2503.01830 | 29 | 2 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.3693 |
dc937d0a4a60d2a361e8c99f76ba5189942868d2bcbe1c0787a63ca623416e48 | [
"arxiv",
"semantic_scholar"
] | A Model-Centric Review of Deep Learning for Protein Design | Deep learning has transformed protein design, enabling accurate structure prediction, sequence optimization, and de novo protein generation. Advances in single-chain protein structure prediction via AlphaFold2, RoseTTAFold, ESMFold, and others have achieved near-experimental accuracy, inspiring successive work extended... | [
"Gregory W. Kyro",
"Tianyin Qiu",
"Victor S. Batista"
] | [
"cs.LG",
"q-bio.BM"
] | [
"Computer Science",
"Biology"
] | 2025-02-26T00:00:00 | https://arxiv.org/abs/2502.19173 | https://arxiv.org/pdf/2502.19173v1 | 2502.19173 | 10.48550/arXiv.2502.19173 | 12 | 0 | false | null | arXiv.org | 0.2785 |
70dfd15729b117583b73dad0dd580ae0bb543c2d623a4a54320969cec05a512d | [
"arxiv",
"semantic_scholar"
] | Leveraging Retrieval-Augmented Generation and Large Language Models to Predict SERCA-Binding Protein Fragments from Cardiac Proteomics Data | Large language models (LLMs) have shown promise in various natural language processing tasks, including their application to proteomics data to classify protein fragments. In this study, we curated a limited mass spectrometry dataset with 1000s of protein fragments, consisting of proteins that appear to be attached to ... | [
"Taylor A Phillips",
"Alejandro W. Huskey",
"Patrick T. Huskey",
"Seth L. Robia",
"Peter M. Kekenes-Huskey"
] | [
"q-bio.QM"
] | [
"Biology"
] | 2025-02-26T00:00:00 | https://arxiv.org/abs/2502.19574 | https://arxiv.org/pdf/2502.19574v1 | 2502.19574 | null | 1 | 0 | true | null | null | 0.084 |
5d813b5a99a83d09a8b8d3effc7cb7748c20890f0a3ad61a59fce3f71ca789b0 | [
"arxiv",
"semantic_scholar"
] | Characterizing the Conformational States of G Protein Coupled Receptors Generated with AlphaFold | G-Protein Coupled Receptors (GPCRs) are integral to numerous physiological processes and are the target of approximately one-third of FDA-approved therapeutics. Despite their significance, only a limited subset of GPCRs has been successfully targeted, primarily due to challenges in accurately modeling their structures.... | [
"Garima Chib",
"Parisa Mollaei",
"Amir Barati Farimani"
] | [
"q-bio.QM",
"q-bio.BM"
] | [
"Biology"
] | 2025-02-24T00:00:00 | https://arxiv.org/abs/2502.17628 | https://arxiv.org/pdf/2502.17628v1 | 2502.17628 | null | 0 | 0 | false | null | null | 0.0437 |
b9ff0a826e0448f586a091edd2a03eed75009cc0bb3434384fca57ab279a9545 | [
"arxiv",
"semantic_scholar"
] | Child vs. machine language learning: Can the logical structure of human language unleash LLMs? | We argue that human language learning proceeds in a manner that is different in nature from current approaches to training LLMs, predicting a difference in learning biases. We then present evidence from German plural formation by LLMs that confirm our hypothesis that even very powerful implementations produce results t... | [
"Uli Sauerland",
"Celia Matthaei",
"Felix Salfner"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-02-24T00:00:00 | https://arxiv.org/abs/2502.17304 | https://arxiv.org/pdf/2502.17304v1 | 2502.17304 | 10.48550/arXiv.2502.17304 | 0 | 0 | false | null | arXiv.org | 0.0688 |
3dbcaca3b663964b2875a938c8fb7a7fd322666f34bf7e758bf43ffc45fdedfe | [
"arxiv",
"semantic_scholar"
] | Integrating protein sequence embeddings with structure via graph-based deep learning for single-residue property prediction | Understanding the intertwined contributions of amino acid sequence and spatial structure is essential to explain protein behaviour. Here, we introduce INFUSSE (Integrated Network Framework Unifying Structure and Sequence Embeddings), a deep learning framework for the prediction of single-residue properties that combine... | [
"Kevin Michalewicz",
"Mauricio Barahona",
"Barbara Bravi"
] | [
"q-bio.QM"
] | [
"Biology"
] | 2025-02-24T00:00:00 | https://arxiv.org/abs/2502.17294 | https://arxiv.org/pdf/2502.17294v2 | 2502.17294 | null | 0 | 0 | false | null | null | 0.0437 |
57eb5718bd228be3ae7fe6d939adc909a22da46c9679fb5e436307041341ca43 | [
"arxiv",
"semantic_scholar"
] | Protein Large Language Models: A Comprehensive Survey | Protein-specific large language models (Protein LLMs) are revolutionizing protein science by enabling more efficient protein structure prediction, function annotation, and design. While existing surveys focus on specific aspects or applications, this work provides the first comprehensive overview of Protein LLMs, cover... | [
"Yijia Xiao",
"Wanjia Zhao",
"Junkai Zhang",
"Yiqiao Jin",
"Han Zhang",
"Zhicheng Ren",
"Renliang Sun",
"Haixin Wang",
"Guancheng Wan",
"Pan Lu",
"Xiao Luo",
"Yu Zhang",
"James Zou",
"Yizhou Sun",
"Wei Wang"
] | [
"q-bio.BM",
"cs.AI",
"cs.CE",
"cs.CL",
"cs.LG"
] | [
"Biology",
"Computer Science"
] | 2025-02-21T00:00:00 | https://arxiv.org/abs/2502.17504 | https://arxiv.org/pdf/2502.17504v2 | 2502.17504 | 10.48550/arXiv.2502.17504 | 40 | 1 | true | https://github.com/Yijia-Xiao/Protein-LLM-Survey | Conference on Empirical Methods in Natural Language Processing | 0.4032 |
47af00d253c454e73ea820d1c675775dd8d636666e4301d3ffd42a403be0e5f8 | [
"arxiv",
"semantic_scholar"
] | Interpreting and Steering Protein Language Models through Sparse Autoencoders | The rapid advancements in transformer-based language models have revolutionized natural language processing, yet understanding the internal mechanisms of these models remains a significant challenge. This paper explores the application of sparse autoencoders (SAE) to interpret the internal representations of protein la... | [
"Edith Natalia Villegas Garcia",
"Alessio Ansuini"
] | [
"cs.LG",
"q-bio.BM"
] | [
"Computer Science",
"Biology"
] | 2025-02-13T00:00:00 | https://arxiv.org/abs/2502.09135 | https://arxiv.org/pdf/2502.09135v1 | 2502.09135 | 10.48550/arXiv.2502.09135 | 18 | 1 | false | null | arXiv.org | 0.3197 |
303e165c21d27a6a599a70ab6c36e4557e3b68d86517fa79ac2c0d6d7d1ac78d | [
"arxiv",
"semantic_scholar"
] | Uncertainty-Aware Adaptation of Large Language Models for Protein-Protein Interaction Analysis | Identification of protein-protein interactions (PPIs) helps derive cellular mechanistic understanding, particularly in the context of complex conditions such as neurodegenerative disorders, metabolic syndromes, and cancer. Large Language Models (LLMs) have demonstrated remarkable potential in predicting protein structu... | [
"Sanket Jantre",
"Tianle Wang",
"Gilchan Park",
"Kriti Chopra",
"Nicholas Jeon",
"Xiaoning Qian",
"Nathan M. Urban",
"Byung-Jun Yoon"
] | [
"cs.LG",
"cs.AI",
"cs.CL",
"stat.AP",
"stat.ML"
] | [
"Medicine",
"Computer Science",
"Mathematics"
] | 2025-02-10T00:00:00 | https://arxiv.org/abs/2502.06173 | https://arxiv.org/pdf/2502.06173v2 | 2502.06173 | 10.1109/EMBC58623.2025.11253873 | 1 | 0 | false | null | Annual International Conference of the IEEE Engineering in Medicine and Biology Society | 0.0753 |
f6d8325db398dd74e2aaab8e9293ccd99b25345344d529d358a9ec3b23e0b94c | [
"arxiv",
"semantic_scholar"
] | PyMOLfold: Interactive Protein and Ligand Structure Prediction in PyMOL | PyMOLfold is a flexible and open-source plugin designed to seamlessly integrate AI-based protein structure prediction and visualization within the widely used PyMOL molecular graphics system. By leveraging state-of-the-art protein folding models such as ESM3, Boltz-1, and Chai-1, PyMOLfold allows researchers to directl... | [
"Colby T. Ford",
"Samee Ullah",
"Dinler Amaral Antunes",
"Tarsis Gesteira Ferreira"
] | [
"q-bio.BM"
] | [
"Biology"
] | 2025-02-01T00:00:00 | https://arxiv.org/abs/2502.00508 | https://arxiv.org/pdf/2502.00508v1 | 2502.00508 | null | 2 | 0 | true | https://github.com/colbyford/PyMolfold | null | 0.1193 |
4a4d7b6fd4fc3a9eab506e5a36c5e0b4c2118d693b368b31118bd64a232403ba | [
"arxiv",
"semantic_scholar"
] | Exploring Large Protein Language Models in Constrained Evaluation Scenarios within the FLIP Benchmark | In this study, we expand upon the FLIP benchmark-designed for evaluating protein fitness prediction models in small, specialized prediction tasks-by assessing the performance of state-of-the-art large protein language models, including ESM-2 and SaProt on the FLIP dataset. Unlike larger, more diverse benchmarks such as... | [
"Manuel F. Mollon",
"Joaquin Gonzalez-Rodriguez",
"Alicia Lozano-Diez",
"Daniel Ramos",
"Doroteo T. Toledano"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-01-30T00:00:00 | https://arxiv.org/abs/2501.18223 | https://arxiv.org/pdf/2501.18223v1 | 2501.18223 | 10.48550/arXiv.2501.18223 | 0 | 0 | false | null | arXiv.org | 0.0401 |
b0e5d7d993174bcfb1cebdeb9d1689de3e86842c878846de1a6d539b68217f7e | [
"arxiv",
"semantic_scholar"
] | Computational Protein Science in the Era of Large Language Models (LLMs) | Considering the significance of proteins, computational protein science has always been a critical scientific field, dedicated to revealing knowledge and developing applications within the protein sequence-structure-function paradigm. In the last few decades, Artificial Intelligence (AI) has made significant impacts in... | [
"Wenqi Fan",
"Yi Zhou",
"Shijie Wang",
"Yuyao Yan",
"Hui Liu",
"Qian Zhao",
"Le Song",
"Qing Li"
] | [
"cs.CE",
"cs.CL",
"q-bio.BM"
] | [
"Computer Science",
"Biology"
] | 2025-01-17T00:00:00 | https://arxiv.org/abs/2501.10282 | https://arxiv.org/pdf/2501.10282v2 | 2501.10282 | 10.48550/arXiv.2501.10282 | 16 | 0 | false | null | arXiv.org | 0.3076 |
4dd4bff106bdb6c57f8d6b5142d6a41fc694c5af63bf5ff70a147a795192300f | [
"arxiv",
"semantic_scholar"
] | Protein Structure Prediction in the 3D HP Model Using Deep Reinforcement Learning | We address protein structure prediction in the 3D Hydrophobic-Polar lattice model through two novel deep learning architectures. For proteins under 36 residues, our hybrid reservoir-based model combines fixed random projections with trainable deep layers, achieving optimal conformations with 25% fewer training episodes... | [
"Giovanny Espitia",
"Yui Tik Pang",
"James C. Gumbart"
] | [
"cs.LG",
"cs.AI",
"q-bio.BM"
] | [
"Computer Science",
"Biology"
] | 2024-12-29T00:00:00 | https://arxiv.org/abs/2412.20329 | https://arxiv.org/pdf/2412.20329v1 | 2412.20329 | 10.48550/arXiv.2412.20329 | 1 | 0 | false | null | arXiv.org | 0.0753 |
5714b0fb38088e67cd504ff5a0cda8334e9b1c6a168a92e2eab53f6a88292473 | [
"arxiv",
"semantic_scholar"
] | PLD-Tree: Persistent Laplacian Decision Tree for Protein-Protein Binding Free Energy Prediction | Recent advances in topology-based modeling have accelerated progress in physical modeling and molecular studies, including applications to protein-ligand binding affinity. In this work, we introduce the Persistent Laplacian Decision Tree (PLD-Tree), a novel method designed to address the challenging task of predicting ... | [
"Xingjian Xu",
"Jiahui Chen",
"Chunmei Wang"
] | [
"q-bio.BM"
] | [
"Biology"
] | 2024-12-24T00:00:00 | https://arxiv.org/abs/2412.18541 | https://arxiv.org/pdf/2412.18541v1 | 2412.18541 | null | 1 | 0 | false | null | null | 0.0753 |
c4d94797fa0a5b6b28fef0c0fb8d493aa6b204656cc77a209eeccf7206683d0b | [
"arxiv",
"semantic_scholar"
] | Overview of the First Workshop on Language Models for Low-Resource Languages (LoResLM 2025) | The first Workshop on Language Models for Low-Resource Languages (LoResLM 2025) was held in conjunction with the 31st International Conference on Computational Linguistics (COLING 2025) in Abu Dhabi, United Arab Emirates. This workshop mainly aimed to provide a forum for researchers to share and discuss their ongoing w... | [
"Hansi Hettiarachchi",
"Tharindu Ranasinghe",
"Paul Rayson",
"Ruslan Mitkov",
"Mohamed Gaber",
"Damith Premasiri",
"Fiona Anting Tan",
"Lasitha Uyangodage"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-12-20T00:00:00 | https://arxiv.org/abs/2412.16365 | https://arxiv.org/pdf/2412.16365v1 | 2412.16365 | 10.48550/arXiv.2412.16365 | 3 | 0 | false | null | null | 0.1505 |
a79ff12487857f34176a6fb403715a3a9fe0c79020386096755e884bf0603029 | [
"arxiv",
"semantic_scholar"
] | Open-Source Protein Language Models for Function Prediction and Protein Design | Protein language models (PLMs) have shown promise in improving the understanding of protein sequences, contributing to advances in areas such as function prediction and protein engineering. However, training these models from scratch requires significant computational resources, limiting their accessibility. To address... | [
"Shivasankaran Vanaja Pandi",
"Bharath Ramsundar"
] | [
"cs.LG",
"q-bio.BM"
] | [
"Computer Science",
"Biology"
] | 2024-12-18T00:00:00 | https://arxiv.org/abs/2412.13519 | https://arxiv.org/pdf/2412.13519v1 | 2412.13519 | 10.48550/arXiv.2412.13519 | 1 | 0 | true | null | arXiv.org | 0.0753 |
da02afb1cc6d2433d6371294eb83bf18719d96fae4a8023ba9519bbcc656f541 | [
"arxiv",
"semantic_scholar"
] | EvoLlama: Enhancing LLMs' Understanding of Proteins via Multimodal Structure and Sequence Representations | Current Large Language Models (LLMs) for understanding proteins primarily treats amino acid sequences as a text modality. Meanwhile, Protein Language Models (PLMs), such as ESM-2, have learned massive sequential evolutionary knowledge from the universe of natural protein sequences. Furthermore, structure-based encoders... | [
"Nuowei Liu",
"Changzhi Sun",
"Tao Ji",
"Junfeng Tian",
"Jianxin Tang",
"Yuanbin Wu",
"Man Lan"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2024-12-16T00:00:00 | https://arxiv.org/abs/2412.11618 | https://arxiv.org/pdf/2412.11618v1 | 2412.11618 | 10.48550/arXiv.2412.11618 | 11 | 1 | false | null | arXiv.org | 0.2698 |
f74aec5afa642777ae4a8f1990d91a7704a2af0e6ff4de75fb3bc2c659816a2b | [
"arxiv",
"semantic_scholar"
] | Applications of Knot Theory for the Improvement of the AlphaFold Protein Database | AlphaFold, a groundbreaking protein prediction model, has revolutionized protein structure prediction, populating the AlphaFold Protein Database (AFDB) with millions of predicted structures. However, AlphaFold's accuracy in predicting proteins with intricate topologies, such as knots, remains a concern. This study inve... | [
"Pranshu Jahagirdar"
] | [
"q-bio.BM"
] | [
"Biology"
] | 2024-12-15T00:00:00 | https://arxiv.org/abs/2412.11229 | https://arxiv.org/pdf/2412.11229v1 | 2412.11229 | null | 1 | 0 | false | null | null | 0.0753 |
2a1a4aa76ffb4c30d9775eb40a07fa694f1b4c0d9a72ee7e102b719dba6a5bce | [
"arxiv",
"semantic_scholar"
] | FlowDock: Geometric Flow Matching for Generative Protein-Ligand Docking and Affinity Prediction | Powerful generative AI models of protein-ligand structure have recently been proposed, but few of these methods support both flexible protein-ligand docking and affinity estimation. Of those that do, none can directly model multiple binding ligands concurrently or have been rigorously benchmarked on pharmacologically r... | [
"Alex Morehead",
"Jianlin Cheng"
] | [
"cs.LG",
"cs.AI",
"q-bio.BM",
"q-bio.QM"
] | [
"Computer Science",
"Biology",
"Medicine"
] | 2024-12-14T00:00:00 | https://arxiv.org/abs/2412.10966 | https://arxiv.org/pdf/2412.10966v3 | 2412.10966 | 10.48550/arXiv.2412.10966 | 13 | 1 | true | https://github.com/BioinfoMachineLearning/FlowDock | arXiv.org | 0.2865 |
9dbae3e1f9681722150f7c7954db91ffc70d27bc3d93c4c3267c0c6313af3675 | [
"arxiv",
"semantic_scholar"
] | KULTURE Bench: A Benchmark for Assessing Language Model in Korean Cultural Context | Large language models have exhibited significant enhancements in performance across various tasks. However, the complexity of their evaluation increases as these models generate more fluent and coherent content. Current multilingual benchmarks often use translated English versions, which may incorporate Western cultura... | [
"Xiaonan Wang",
"Jinyoung Yeo",
"Joon-Ho Lim",
"Hansaem Kim"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-12-10T00:00:00 | https://arxiv.org/abs/2412.07251 | https://arxiv.org/pdf/2412.07251v1 | 2412.07251 | 10.48550/arXiv.2412.07251 | 6 | 0 | false | null | Pacific Asia Conference on Language, Information and Computation | 0.2113 |
92a148adcd16723d97a819dd593838620df3c4010f08c136b6389a5c0a83a9ef | [
"arxiv",
"semantic_scholar"
] | Leveraging Multi-modal Representations to Predict Protein Melting Temperatures | Accurately predicting protein melting temperature changes (Delta Tm) is fundamental for assessing protein stability and guiding protein engineering. Leveraging multi-modal protein representations has shown great promise in capturing the complex relationships among protein sequences, structures, and functions. In this s... | [
"Daiheng Zhang",
"Yan Zeng",
"Xinyu Hong",
"Jinbo Xu"
] | [
"cs.LG",
"cs.CE"
] | [
"Computer Science"
] | 2024-12-05T00:00:00 | https://arxiv.org/abs/2412.04526 | https://arxiv.org/pdf/2412.04526v3 | 2412.04526 | null | 0 | 0 | false | null | null | 0 |
5416b975d46237d4d056c15cb231a8063181b6565ab14588563889f7a68f67e1 | [
"arxiv",
"semantic_scholar"
] | SeqProFT: Sequence-only Protein Property Prediction with LoRA Finetuning | Protein language models (PLMs) have demonstrated remarkable capabilities in learning relationships between protein sequences and functions. However, finetuning these large models requires substantial computational resources, often with suboptimal task-specific results. This study investigates how parameter-efficient fi... | [
"Shuo Zhang",
"Jian K. Liu"
] | [
"cs.LG",
"q-bio.QM"
] | [
"Computer Science",
"Biology"
] | 2024-11-18T00:00:00 | https://arxiv.org/abs/2411.11530 | https://arxiv.org/pdf/2411.11530v2 | 2411.11530 | 10.1109/TAI.2025.3636109 | 4 | 0 | true | https://github.com/jiankliu/SeqProFT | IEEE Transactions on Artificial Intelligence | 0.1747 |
90e93b88184849bb479076fe77405271bc9d370b6d38a871ba20aea98fea2d0a | [
"arxiv",
"semantic_scholar"
] | SCOP: A Sequence-Structure Contrast-Aware Framework for Protein Function Prediction | Improving the ability to predict protein function can potentially facilitate research in the fields of drug discovery and precision medicine. Technically, the properties of proteins are directly or indirectly reflected in their sequence and structure information, especially as the protein function is largely determined... | [
"Runze Ma",
"Chengxin He",
"Huiru Zheng",
"Xinye Wang",
"Haiying Wang",
"Yidan Zhang",
"Lei Duan"
] | [
"q-bio.BM"
] | [
"Computer Science",
"Biology"
] | 2024-11-18T00:00:00 | https://arxiv.org/abs/2411.11366 | https://arxiv.org/pdf/2411.11366v1 | 2411.11366 | 10.1109/BIBM62325.2024.10822541 | 1 | 0 | false | null | IEEE International Conference on Bioinformatics and Biomedicine | 0.0753 |
31ba7fddaa9c71b0e881eab08751a1f7bcf20153612fe38fa60433e5d6982699 | [
"arxiv",
"semantic_scholar"
] | Efficient Alignment of Large Language Models via Data Sampling | LLM alignment ensures that large language models behave safely and effectively by aligning their outputs with human values, goals, and intentions. Aligning LLMs employ huge amounts of data, computation, and time. Moreover, curating data with human feedback is expensive and takes time. Recent research depicts the benefi... | [
"Amrit Khera",
"Rajat Ghosh",
"Debojyoti Dutta"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2024-11-15T00:00:00 | https://arxiv.org/abs/2411.10545 | https://arxiv.org/pdf/2411.10545v2 | 2411.10545 | 10.48550/arXiv.2411.10545 | 1 | 0 | false | null | null | 0.0753 |
a6196dfd3b26c44d9b71ee21cc182fc4b60eb62bc8d3fe9bee843592c2d369e2 | [
"arxiv",
"semantic_scholar"
] | InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders | Protein language models (PLMs) have demonstrated remarkable success in protein modeling and design, yet their internal mechanisms for predicting structure and function remain poorly understood. Here we present a systematic approach to extract and analyze interpretable features from PLMs using sparse autoencoders (SAEs)... | [
"Elana Simon",
"James Zou"
] | [
"q-bio.BM",
"cs.AI",
"cs.LG",
"q-bio.QM"
] | [
"Computer Science",
"Medicine",
"Biology"
] | 2024-11-13T00:00:00 | https://arxiv.org/abs/2412.12101 | https://arxiv.org/pdf/2412.12101v1 | 2412.12101 | 10.1038/s41592-025-02836-7 | 107 | 5 | true | null | bioRxiv | 0.5084 |
1ee48b8b8781ea84f3200b1c2580b536d520d46ebdfaa70f7227894b7ea747a4 | [
"arxiv",
"semantic_scholar"
] | Concept Bottleneck Language Models For protein design | We introduce Concept Bottleneck Protein Language Models (CB-pLM), a generative masked language model with a layer where each neuron corresponds to an interpretable concept. Our architecture offers three key benefits: i) Control: We can intervene on concept values to precisely control the properties of generated protein... | [
"Aya Abdelsalam Ismail",
"Tuomas Oikarinen",
"Amy Wang",
"Julius Adebayo",
"Samuel Stanton",
"Taylor Joren",
"Joseph Kleinhenz",
"Allen Goodman",
"Héctor Corrada Bravo",
"Kyunghyun Cho",
"Nathan C. Frey"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-11-09T00:00:00 | https://arxiv.org/abs/2411.06090 | https://arxiv.org/pdf/2411.06090v2 | 2411.06090 | 10.48550/arXiv.2411.06090 | 24 | 0 | false | null | International Conference on Learning Representations | 0.3495 |
f19ddee6ca9dfecf59de4a9effff5d147bb3504ee4505fe0c22090f03718c574 | [
"arxiv",
"semantic_scholar"
] | Energy Efficient Protein Language Models: Leveraging Small Language Models with LoRA for Controllable Protein Generation | Large language models (LLMs) have demonstrated significant success in natural language processing (NLP) tasks and have shown promising results in other domains such as protein sequence generation. However, there remain salient differences between LLMs used for NLP, which effectively handle multiple tasks and are availa... | [
"Aayush Shah",
"Shankar Jayaratnam"
] | [
"q-bio.BM",
"cs.LG"
] | [
"Biology",
"Computer Science"
] | 2024-11-08T00:00:00 | https://arxiv.org/abs/2411.05966 | https://arxiv.org/pdf/2411.05966v1 | 2411.05966 | 10.48550/arXiv.2411.05966 | 2 | 0 | false | null | arXiv.org | 0.1193 |
f6a78c51e25b618cdf6668130ab0e6d6b75c1435d961ff36577b0b4dd3cc826a | [
"arxiv",
"semantic_scholar"
] | Training Compute-Optimal Protein Language Models | We explore optimally training protein language models, an area of significant interest in biological research where guidance on best practices is limited. Most models are trained with extensive compute resources until performance gains plateau, focusing primarily on increasing model sizes rather than optimizing the eff... | [
"Xingyi Cheng",
"Bo Chen",
"Pan Li",
"Jing Gong",
"Jie Tang",
"Le Song"
] | [
"cs.LG",
"cs.AI",
"q-bio.QM"
] | [
"Biology",
"Computer Science"
] | 2024-11-04T00:00:00 | https://arxiv.org/abs/2411.02142 | https://arxiv.org/pdf/2411.02142v1 | 2411.02142 | 10.1101/2024.06.06.597716 | 38 | 4 | true | https://github.com/cxysteven/ScalingProteinLM | bioRxiv | 0.3978 |
bca3ef1d13537ca06fb99bfd4e47b715e2654c640fbdfbf472912ace0cb5657f | [
"arxiv",
"semantic_scholar"
] | Beyond Current Boundaries: Integrating Deep Learning and AlphaFold for Enhanced Protein Structure Prediction from Low-Resolution Cryo-EM Maps | Constructing atomic models from cryo-electron microscopy (cryo-EM) maps is a crucial yet intricate task in structural biology. While advancements in deep learning, such as convolutional neural networks (CNNs) and graph neural networks (GNNs), have spurred the development of sophisticated map-to-model tools like DeepTra... | [
" Xin",
" Ma",
"Dong Si"
] | [
"q-bio.QM",
"cs.LG"
] | [
"Medicine",
"Computer Science",
"Biology"
] | 2024-10-30T00:00:00 | https://arxiv.org/abs/2410.23321 | https://arxiv.org/pdf/2410.23321v1 | 2410.23321 | 10.48550/arXiv.2410.23321 | 4 | 0 | false | null | null | 0.1747 |
4b418cb0afd769cfc3539cd6b4851f7765e704a22428474154cc522e8dcdf0bc | [
"arxiv",
"semantic_scholar"
] | MutaPLM: Protein Language Modeling for Mutation Explanation and Engineering | Studying protein mutations within amino acid sequences holds tremendous significance in life sciences. Protein language models (PLMs) have demonstrated strong capabilities in broad biological applications. However, due to architectural design and lack of supervision, PLMs model mutations implicitly with evolutionary pl... | [
"Yizhen Luo",
"Zikun Nie",
"Massimo Hong",
"Suyuan Zhao",
"Hao Zhou",
"Zaiqing Nie"
] | [
"cs.LG",
"q-bio.BM"
] | [
"Computer Science",
"Biology"
] | 2024-10-30T00:00:00 | https://arxiv.org/abs/2410.22949 | https://arxiv.org/pdf/2410.22949v1 | 2410.22949 | 10.48550/arXiv.2410.22949 | 2 | 0 | true | https://github.com/PharMolix/MutaPLM | Neural Information Processing Systems | 0.1193 |
4d1937dbb43c35b72917e22fa5d43494a278092c9a0a438f595b1af870353460 | [
"arxiv",
"semantic_scholar"
] | Long-context Protein Language Modeling Using Bidirectional Mamba with Shared Projection Layers | Self-supervised training of language models (LMs) has seen great success for protein sequences in learning meaningful representations and for generative drug design. Most protein LMs are based on the Transformer architecture trained on individual proteins with short context lengths. Such protein LMs cannot extrapolate ... | [
"Yingheng Wang",
"Zichen Wang",
"Gil Sadeh",
"Luca Zancato",
"Alessandro Achille",
"George Karypis",
"Huzefa Rangwala"
] | [
"q-bio.BM",
"cs.LG"
] | [
"Biology",
"Computer Science"
] | 2024-10-29T00:00:00 | https://arxiv.org/abs/2411.08909 | https://arxiv.org/pdf/2411.08909v3 | 2411.08909 | 10.1101/2024.10.29.620988 | 4 | 0 | true | https://github.com/amazon-science/LC-PLM | bioRxiv | 0.1747 |
1bb1d3b322321c1932517be23b35eb81171f1d7f412fcdcf84958dfaec705f3b | [
"arxiv",
"semantic_scholar"
] | Retrieval-Enhanced Mutation Mastery: Augmenting Zero-Shot Prediction of Protein Language Model | Enzyme engineering enables the modification of wild-type proteins to meet industrial and research demands by enhancing catalytic activity, stability, binding affinities, and other properties. The emergence of deep learning methods for protein modeling has demonstrated superior results at lower costs compared to traditi... | [
"Yang Tan",
"Ruilin Wang",
"Banghao Wu",
"Liang Hong",
"Bingxin Zhou"
] | [
"cs.CL",
"cs.AI",
"q-bio.QM"
] | [
"Computer Science",
"Biology"
] | 2024-10-28T00:00:00 | https://arxiv.org/abs/2410.21127 | https://arxiv.org/pdf/2410.21127v1 | 2410.21127 | 10.48550/arXiv.2410.21127 | 21 | 1 | true | https://github.com/tyang816/ProtREM | arXiv.org | 0.3356 |
9c3e96e327d50b157e6bd8930a71fc5bc468bb8311d27b0f21f6a802d928ce03 | [
"arxiv",
"semantic_scholar"
] | A Survey of Large Language Models for Arabic Language and its Dialects | This survey offers a comprehensive overview of Large Language Models (LLMs) designed for Arabic language and its dialects. It covers key architectures, including encoder-only, decoder-only, and encoder-decoder models, along with the datasets used for pre-training, spanning Classical Arabic, Modern Standard Arabic, and ... | [
"Malak Mashaabi",
"Shahad Al-Khalifa",
"Hend Al-Khalifa"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-10-26T00:00:00 | https://arxiv.org/abs/2410.20238 | https://arxiv.org/pdf/2410.20238v2 | 2410.20238 | 10.1145/3807946 | 23 | 1 | false | null | ACM Transactions on Asian and Low-Resource Language Information Processing | 0.3451 |
1ca54675e685546beaf239d1e8e851f5ee61dc5ac0b2824e6f944432ab22a28b | [
"arxiv",
"semantic_scholar"
] | Structure Language Models for Protein Conformation Generation | Proteins adopt multiple structural conformations to perform their diverse biological functions, and understanding these conformations is crucial for advancing drug discovery. Traditional physics-based simulation methods often struggle with sampling equilibrium conformations and are computationally expensive. Recently, ... | [
"Jiarui Lu",
"Xiaoyin Chen",
"Stephen Zhewen Lu",
"Chence Shi",
"Hongyu Guo",
"Yoshua Bengio",
"Jian Tang"
] | [
"q-bio.BM",
"cs.LG"
] | [
"Computer Science",
"Biology"
] | 2024-10-24T00:00:00 | https://arxiv.org/abs/2410.18403 | https://arxiv.org/pdf/2410.18403v2 | 2410.18403 | 10.48550/arXiv.2410.18403 | 30 | 1 | false | null | International Conference on Learning Representations | 0.3728 |
cf534dfc404fc56bfb66af22137ded7c996248edfba10408a32eb3f94f313d99 | [
"arxiv",
"semantic_scholar"
] | CPE-Pro: A Structure-Sensitive Deep Learning Method for Protein Representation and Origin Evaluation | Protein structures are important for understanding their functions and interactions. Currently, many protein structure prediction methods are enriching the structure database. Discriminating the origin of structures is crucial for distinguishing between experimentally resolved and computationally predicted structures, ... | [
"Wenrui Gou",
"Wenhui Ge",
"Yang Tan",
"Mingchen Li",
"Guisheng Fan",
"Huiqun Yu"
] | [
"q-bio.BM",
"cs.CL",
"cs.LG",
"q-bio.QM"
] | [
"Biology",
"Computer Science",
"Medicine"
] | 2024-10-21T00:00:00 | https://arxiv.org/abs/2410.15592 | https://arxiv.org/pdf/2410.15592v2 | 2410.15592 | 10.1007/s12539-025-00732-4 | 2 | 0 | true | https://github.com/GouWenrui/CPE-Pro-main.git | Interdisciplinary Sciences Computational Life Sciences | 0.1193 |
97f43958cb9ba379473dc0a3277c4478f82e334a039107dafb8a4a31b470edfa | [
"arxiv",
"semantic_scholar"
] | Proteins with alternative folds reveal blind spots in AlphaFold-based protein structure prediction | In recent years, advances in artificial intelligence (AI) have transformed structural biology, particularly protein structure prediction. Though AI-based methods, such as AlphaFold (AF), often predict single conformations of proteins with high accuracy and confidence, predictions of alternative folds are often inaccura... | [
"Devlina Chakravarty",
"Myeongsang Lee",
"Lauren L. Porter"
] | [
"q-bio.BM"
] | [
"Biology",
"Medicine"
] | 2024-10-18T00:00:00 | https://arxiv.org/abs/2410.14898 | https://arxiv.org/pdf/2410.14898v1 | 2410.14898 | null | 25 | 0 | false | null | arXiv.org | 0.3537 |
109fd66ea0e6363514757dd867feb8869a40c4bf05ac6133b32f20b139acae0a | [
"arxiv",
"semantic_scholar"
] | DPLM-2: A Multimodal Diffusion Protein Language Model | Proteins are essential macromolecules defined by their amino acid sequences, which determine their three-dimensional structures and, consequently, their functions in all living organisms. Therefore, generative protein modeling necessitates a multimodal approach to simultaneously model, understand, and generate both seq... | [
"Xinyou Wang",
"Zaixiang Zheng",
"Fei Ye",
"Dongyu Xue",
"Shujian Huang",
"Quanquan Gu"
] | [
"cs.LG",
"q-bio.QM"
] | [
"Computer Science",
"Biology"
] | 2024-10-17T00:00:00 | https://arxiv.org/abs/2410.13782 | https://arxiv.org/pdf/2410.13782v1 | 2410.13782 | 10.48550/arXiv.2410.13782 | 72 | 8 | false | null | International Conference on Learning Representations | 0.4771 |
b7e06740c37266946cf41d752dfee1a8a17b8a311ad1674013be22389d475f8b | [
"arxiv",
"semantic_scholar"
] | Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learning | Graph representation learning, involving both node features and graph structures, is crucial for real-world applications but often encounters pervasive noise. State-of-the-art methods typically address noise by focusing separately on node features with large language models (LLMs) and on graph structures with graph str... | [
"Guangxin Su",
"Yifan Zhu",
"Wenjie Zhang",
"Hanchen Wang",
"Ying Zhang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2024-10-15T00:00:00 | https://arxiv.org/abs/2410.12096 | https://arxiv.org/pdf/2410.12096v1 | 2410.12096 | 10.48550/arXiv.2410.12096 | 3 | 1 | false | null | arXiv.org | 0.1505 |
371cf8ac654a825410b42abbfe095a6b8b1ae4b3b590ba384882cca6ff404f1a | [
"arxiv",
"semantic_scholar"
] | Model-based Large Language Model Customization as Service | Prominent Large Language Model (LLM) services from providers like OpenAI and Google excel at general tasks but often underperform on domain-specific applications. Current customization services for these LLMs typically require users to upload data for fine-tuning, posing significant privacy risks. While differentially ... | [
"Zhaomin Wu",
"Jizhou Guo",
"Junyi Hou",
"Bingsheng He",
"Lixin Fan",
"Qiang Yang"
] | [
"cs.LG",
"cs.AI",
"cs.CR"
] | [
"Computer Science"
] | 2024-10-14T00:00:00 | https://arxiv.org/abs/2410.10481 | https://arxiv.org/pdf/2410.10481v5 | 2410.10481 | 10.18653/v1/2025.emnlp-main.248 | 2 | 0 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.1193 |
6467666eaa8ea3ab6346a96ba43c19a6ff5c79cd9515cd7d41a049297e0183b8 | [
"arxiv",
"semantic_scholar"
] | From N-grams to Pre-trained Multilingual Models For Language Identification | In this paper, we investigate the use of N-gram models and Large Pre-trained Multilingual models for Language Identification (LID) across 11 South African languages. For N-gram models, this study shows that effective data size selection remains crucial for establishing effective frequency distributions of the target la... | [
"Thapelo Sindane",
"Vukosi Marivate"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-10-11T00:00:00 | https://arxiv.org/abs/2410.08728 | https://arxiv.org/pdf/2410.08728v1 | 2410.08728 | 10.48550/arXiv.2410.08728 | 5 | 1 | false | null | null | 0.1945 |
d6200f974bff103076f9972960728197bbf44cdf6d5796bf7027abcd8b8d0570 | [
"arxiv",
"semantic_scholar"
] | Metalic: Meta-Learning In-Context with Protein Language Models | Predicting the biophysical and functional properties of proteins is essential for in silico protein design. Machine learning has emerged as a promising technique for such prediction tasks. However, the relative scarcity of in vitro annotations means that these models often have little, or no, specific data on the desir... | [
"Jacob Beck",
"Shikha Surana",
"Manus McAuliffe",
"Oliver Bent",
"Thomas D. Barrett",
"Juan Jose Garau Luis",
"Paul Duckworth"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-10-10T00:00:00 | https://arxiv.org/abs/2410.08355 | https://arxiv.org/pdf/2410.08355v3 | 2410.08355 | 10.48550/arXiv.2410.08355 | 4 | 0 | true | https://github.com/instadeepai/metalic | International Conference on Learning Representations | 0.1747 |
4bb659bee2033032c6c6bfa8f48c3349746dc8d24dd274b38a3c47ac10903f6c | [
"arxiv",
"semantic_scholar"
] | Structure-Enhanced Protein Instruction Tuning: Towards General-Purpose Protein Understanding with LLMs | Proteins, as essential biomolecules, play a central role in biological processes, including metabolic reactions and DNA replication. Accurate prediction of their properties and functions is crucial in biological applications. Recent development of protein language models (pLMs) with supervised fine tuning provides a pr... | [
"Wei Wu",
"Chao Wang",
"Liyi Chen",
"Mingze Yin",
"Yiheng Zhu",
"Kun Fu",
"Jieping Ye",
"Hui Xiong",
"Zheng Wang"
] | [
"cs.CL",
"q-bio.BM"
] | [
"Computer Science",
"Biology"
] | 2024-10-04T00:00:00 | https://arxiv.org/abs/2410.03553 | https://arxiv.org/pdf/2410.03553v3 | 2410.03553 | 10.1145/3711896.3737138 | 6 | 0 | true | null | Knowledge Discovery and Data Mining | 0.2113 |
cba57da086e8c65f4a0251179f68c1cc0d6fe39cc7c3a106d40768137b06ce60 | [
"arxiv",
"semantic_scholar"
] | Function-Guided Conditional Generation Using Protein Language Models with Adapters | The conditional generation of proteins with desired functions is a key goal for generative models. Existing methods based on prompting of protein language models (PLMs) can generate proteins conditioned on a target functionality, such as a desired enzyme family. However, these methods are limited to simple, tokenized c... | [
"Jason Yang",
"Aadyot Bhatnagar",
"Jeffrey A. Ruffolo",
"Ali Madani"
] | [
"q-bio.BM",
"cs.LG"
] | [
"Biology",
"Computer Science"
] | 2024-10-04T00:00:00 | https://arxiv.org/abs/2410.03634 | https://arxiv.org/pdf/2410.03634v2 | 2410.03634 | null | 9 | 0 | false | null | null | 0.25 |
e895f6c0ed280a291504ffa368015646f7fa71278ddabeab8ddbf804ff229e54 | [
"arxiv",
"semantic_scholar"
] | Morphological evaluation of subwords vocabulary used by BETO language model | Subword tokenization algorithms used by Large Language Models are significantly more efficient and can independently build the necessary vocabulary of words and subwords without human intervention. However, those subwords do not always align with real morphemes, potentially impacting the models' performance, though it ... | [
"Óscar García-Sierra",
"Ana Fernández-Pampillón Cesteros",
"Miguel Ortega-Martín"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-10-03T00:00:00 | https://arxiv.org/abs/2410.02283 | https://arxiv.org/pdf/2410.02283v1 | 2410.02283 | 10.48550/arXiv.2410.02283 | 0 | 0 | false | null | arXiv.org | 0 |
6653216f5243e6956c8eba87dca5a675122c6bf85aa465d5904d969d2bb1426e | [
"arxiv",
"semantic_scholar"
] | Recent advances in interpretable machine learning using structure-based protein representations | Recent advancements in machine learning (ML) are transforming the field of structural biology. For example, AlphaFold, a groundbreaking neural network for protein structure prediction, has been widely adopted by researchers. The availability of easy-to-use interfaces and interpretable outcomes from the neural network a... | [
"Luiz Felipe Vecchietti",
"Minji Lee",
"Begench Hangeldiyev",
"Hyunkyu Jung",
"Hahnbeom Park",
"Tae-Kyun Kim",
"Meeyoung Cha",
"Ho Min Kim"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-09-26T00:00:00 | https://arxiv.org/abs/2409.17726 | https://arxiv.org/pdf/2409.17726v1 | 2409.17726 | 10.48550/arXiv.2409.17726 | 1 | 0 | false | null | arXiv.org | 0.0753 |
822045c4adce0b15601f705784e562244288128e3d3397ae9d316bef326b9a66 | [
"arxiv",
"semantic_scholar"
] | Behavioral Bias of Vision-Language Models: A Behavioral Finance View | Large Vision-Language Models (LVLMs) evolve rapidly as Large Language Models (LLMs) was equipped with vision modules to create more human-like models. However, we should carefully evaluate their applications in different domains, as they may possess undesired biases. Our work studies the potential behavioral biases of ... | [
"Yuhang Xiao",
"Yudi Lin",
"Ming-Chang Chiu"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-09-23T00:00:00 | https://arxiv.org/abs/2409.15256 | https://arxiv.org/pdf/2409.15256v1 | 2409.15256 | 10.48550/arXiv.2409.15256 | 3 | 0 | true | https://github.com/mydcxiao/vlm_behavioral_fin | arXiv.org | 0.1505 |
46bb5830a9e81b4015dc6e808d5e63abf6fa7f98268b979053136b6c4938df4e | [
"arxiv",
"semantic_scholar"
] | Protein-Mamba: Biological Mamba Models for Protein Function Prediction | Protein function prediction is a pivotal task in drug discovery, significantly impacting the development of effective and safe therapeutics. Traditional machine learning models often struggle with the complexity and variability inherent in predicting protein functions, necessitating more sophisticated approaches. In th... | [
"Bohao Xu",
"Yingzhou Lu",
"Yoshitaka Inoue",
"Namkyeong Lee",
"Tianfan Fu",
"Jintai Chen"
] | [
"cs.LG",
"q-bio.BM",
"q-bio.QM"
] | [
"Computer Science",
"Biology"
] | 2024-09-22T00:00:00 | https://arxiv.org/abs/2409.14617 | https://arxiv.org/pdf/2409.14617v1 | 2409.14617 | 10.48550/arXiv.2409.14617 | 5 | 0 | false | null | arXiv.org | 0.1945 |
3d2f98480d25274dd22e67d0520faca7df349312af9e7a5f1d1b3e0b2e9e36ae | [
"arxiv",
"semantic_scholar"
] | Natural Language Processing Methods for the Study of Protein-Ligand Interactions | Recent advances in Natural Language Processing (NLP) have ignited interest in developing effective methods for predicting protein-ligand interactions (PLIs) given their relevance to drug discovery and protein engineering efforts and the ever-growing volume of biochemical sequence and structural data available. The para... | [
"James Michels",
"Ramya Bandarupalli",
"Amin Ahangar Akbari",
"Thai Le",
"Hong Xiao",
"Jing Li",
"Erik F. Y. Hom"
] | [
"q-bio.QM",
"cs.CL"
] | [
"Biology",
"Computer Science",
"Medicine"
] | 2024-09-19T00:00:00 | https://arxiv.org/abs/2409.13057 | https://arxiv.org/pdf/2409.13057v2 | 2409.13057 | 10.48550/arXiv.2409.13057 | 3 | 0 | false | null | arXiv.org | 0.1505 |
53d85d931d50a96a4bd3812b2f0fbe7715b89b477235fea0485d655e5994f70c | [
"arxiv",
"semantic_scholar"
] | THaMES: An End-to-End Tool for Hallucination Mitigation and Evaluation in Large Language Models | Hallucination, the generation of factually incorrect content, is a growing challenge in Large Language Models (LLMs). Existing detection and mitigation methods are often isolated and insufficient for domain-specific needs, lacking a standardized pipeline. This paper introduces THaMES (Tool for Hallucination Mitigations... | [
"Mengfei Liang",
"Archish Arun",
"Zekun Wu",
"Cristian Munoz",
"Jonathan Lutch",
"Emre Kazim",
"Adriano Koshiyama",
"Philip Treleaven"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-09-17T00:00:00 | https://arxiv.org/abs/2409.11353 | https://arxiv.org/pdf/2409.11353v3 | 2409.11353 | 10.48550/arXiv.2409.11353 | 8 | 1 | false | null | arXiv.org | 0.2386 |
2e4e3ecd950a0b6aa0f08a9f06a0b7f9c3110fb36cb78010805e944f99c756c7 | [
"arxiv",
"semantic_scholar"
] | Unforgettable Generalization in Language Models | When language models (LMs) are trained to forget (or "unlearn'') a skill, how precisely does their behavior change? We study the behavior of transformer LMs in which tasks have been forgotten via fine-tuning on randomized labels. Such LMs learn to generate near-random predictions for individual examples in the "trainin... | [
"Eric Zhang",
"Leshem Chosen",
"Jacob Andreas"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2024-09-03T00:00:00 | https://arxiv.org/abs/2409.02228 | https://arxiv.org/pdf/2409.02228v1 | 2409.02228 | 10.48550/arXiv.2409.02228 | 4 | 0 | false | null | arXiv.org | 0.1747 |
ebfc22335033f3e4ddfe7992f30a7defe5901bcc82e19a507fcdb5ba92dff938 | [
"arxiv",
"semantic_scholar"
] | SpeechPrompt: Prompting Speech Language Models for Speech Processing Tasks | Prompting has become a practical method for utilizing pre-trained language models (LMs). This approach offers several advantages. It allows an LM to adapt to new tasks with minimal training and parameter updates, thus achieving efficiency in both storage and computation. Additionally, prompting modifies only the LM's i... | [
"Kai-Wei Chang",
"Haibin Wu",
"Yu-Kai Wang",
"Yuan-Kuei Wu",
"Hua Shen",
"Wei-Cheng Tseng",
"Iu-thing Kang",
"Shang-Wen Li",
"Hung-yi Lee"
] | [
"eess.AS",
"cs.AI",
"cs.CL",
"cs.LG"
] | [
"Engineering",
"Computer Science"
] | 2024-08-23T00:00:00 | https://arxiv.org/abs/2408.13040 | https://arxiv.org/pdf/2408.13040v1 | 2408.13040 | 10.1109/TASLP.2024.3436618 | 17 | 1 | false | null | IEEE/ACM Transactions on Audio Speech and Language Processing | 0.3138 |
cf287b087371d8417a98f91edf87975972b5db6c9bf4b4e7e19d55f75fe2bf4c | [
"arxiv",
"semantic_scholar"
] | AlphaFolding: 4D Diffusion for Dynamic Protein Structure Prediction with Reference and Motion Guidance | Protein structure prediction is pivotal for understanding the structure-function relationship of proteins, advancing biological research, and facilitating pharmaceutical development and experimental design. While deep learning methods and the expanded availability of experimental 3D protein structures have accelerated ... | [
"Kaihui Cheng",
"Ce Liu",
"Qingkun Su",
"Jun Wang",
"Liwei Zhang",
"Yining Tang",
"Yao Yao",
"Siyu Zhu",
"Yuan Qi"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2024-08-22T00:00:00 | https://arxiv.org/abs/2408.12419 | https://arxiv.org/pdf/2408.12419v3 | 2408.12419 | null | 4 | 0 | false | null | null | 0.1747 |
a4c973dafd55796e28b7623e472283d1ee8ed9ff823d67c38df0beb16701660d | [
"arxiv",
"semantic_scholar"
] | CoPRA: Bridging Cross-domain Pretrained Sequence Models with Complex Structures for Protein-RNA Binding Affinity Prediction | Accurately measuring protein-RNA binding affinity is crucial in many biological processes and drug design. Previous computational methods for protein-RNA binding affinity prediction rely on either sequence or structure features, unable to capture the binding mechanisms comprehensively. The recent emerging pre-trained l... | [
"Rong Han",
"Xiaohong Liu",
"Tong Pan",
"Jing Xu",
"Xiaoyu Wang",
"Wuyang Lan",
"Zhenyu Li",
"Zixuan Wang",
"Jiangning Song",
"Guangyu Wang",
"Ting Chen"
] | [
"q-bio.BM",
"cs.LG"
] | [
"Biology",
"Computer Science"
] | 2024-08-21T00:00:00 | https://arxiv.org/abs/2409.03773 | https://arxiv.org/pdf/2409.03773v2 | 2409.03773 | 10.48550/arXiv.2409.03773 | 8 | 0 | false | null | AAAI Conference on Artificial Intelligence | 0.2386 |
ea2979422e2bdc93ee3dc87bcd50692b63747540fec3bdada2be465162838aed | [
"arxiv",
"semantic_scholar"
] | ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure Understanding | Understanding biological processes, drug development, and biotechnological advancements requires a detailed analysis of protein structures and functions, a task that is inherently complex and time-consuming in traditional protein research. To streamline this process, we introduce ProteinGPT, a state-of-the-art multimod... | [
"Yijia Xiao",
"Edward Sun",
"Yiqiao Jin",
"Qifan Wang",
"Wei Wang"
] | [
"cs.AI",
"cs.CE",
"cs.LG",
"q-bio.BM"
] | [
"Computer Science",
"Biology"
] | 2024-08-21T00:00:00 | https://arxiv.org/abs/2408.11363 | https://arxiv.org/pdf/2408.11363v2 | 2408.11363 | 10.48550/arXiv.2408.11363 | 43 | 1 | true | https://github.com/ProteinGPT/ProteinGPT | arXiv.org | 0.4109 |
29c7678109761d95ff7948a07cab9481d71e13940635c287517cc2d09a908843 | [
"arxiv",
"semantic_scholar"
] | Design Proteins Using Large Language Models: Enhancements and Comparative Analyses | Pre-trained LLMs have demonstrated substantial capabilities across a range of conventional natural language processing (NLP) tasks, such as summarization and entity recognition. In this paper, we explore the application of LLMs in the generation of high-quality protein sequences. Specifically, we adopt a suite of pre-t... | [
"Kamyar Zeinalipour",
"Neda Jamshidi",
"Monica Bianchini",
"Marco Maggini",
"Marco Gori"
] | [
"q-bio.QM",
"cs.AI",
"cs.LG"
] | [
"Computer Science",
"Biology"
] | 2024-08-12T00:00:00 | https://arxiv.org/abs/2408.06396 | https://arxiv.org/pdf/2408.06396v1 | 2408.06396 | 10.18653/v1/2024.langmol-1.5 | 3 | 0 | false | null | null | 0.1505 |
ba042b02e1a3d3ba3fe6eb0a4149c05d980b433e1adff6ec26b837721c84b24d | [
"arxiv",
"semantic_scholar"
] | Peptide Sequencing Via Protein Language Models | We introduce a protein language model for determining the complete sequence of a peptide based on measurement of a limited set of amino acids. To date, protein sequencing relies on mass spectrometry, with some novel edman degregation based platforms able to sequence non-native peptides. Current protein sequencing techn... | [
"Thuong Le Hoai Pham",
"Jillur Rahman Saurav",
"Aisosa A. Omere",
"Calvin J. Heyl",
"Mohammad Sadegh Nasr",
"Cody Tyler Reynolds",
"Jai Prakash Yadav Veerla",
"Helen H Shang",
"Justyn Jaworski",
"Alison Ravenscraft",
"Joseph Anthony Buonomo",
"Jacob M. Luber"
] | [
"q-bio.BM",
"cs.LG"
] | [
"Computer Science",
"Biology"
] | 2024-08-01T00:00:00 | https://arxiv.org/abs/2408.00892 | https://arxiv.org/pdf/2408.00892v1 | 2408.00892 | 10.1145/3698587.3701385 | 2 | 0 | false | null | ACM International Conference on Bioinformatics, Computational Biology and Biomedicine | 0.1193 |
87429a29a88e563ad1de849fe67007d3fca8bc5ad613b4fcbe96060ea2508fc3 | [
"arxiv",
"semantic_scholar"
] | Improving Text Embeddings for Smaller Language Models Using Contrastive Fine-tuning | While Large Language Models show remarkable performance in natural language understanding, their resource-intensive nature makes them less accessible. In contrast, smaller language models such as MiniCPM offer more sustainable scalability, but often underperform without specialized optimization. In this paper, we explo... | [
"Trapoom Ukarapol",
"Zhicheng Lee",
"Amy Xin"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-08-01T00:00:00 | https://arxiv.org/abs/2408.00690 | https://arxiv.org/pdf/2408.00690v2 | 2408.00690 | 10.48550/arXiv.2408.00690 | 3 | 0 | true | https://github.com/trapoom555/Language-Model-STS-CFT | arXiv.org | 0.1505 |
eb3655e9400f731cf47d3228deb7238875ed1df66c571b3d98d06662301a2762 | [
"arxiv",
"semantic_scholar"
] | Ranking protein-protein models with large language models and graph neural networks | Protein-protein interactions (PPIs) are associated with various diseases, including cancer, infections, and neurodegenerative disorders. Obtaining three-dimensional structural information on these PPIs serves as a foundation to interfere with those or to guide drug design. Various strategies can be followed to model th... | [
"Xiaotong Xu",
"Alexandre M. J. J. Bonvin"
] | [
"q-bio.BM",
"cs.AI"
] | [
"Medicine",
"Biology",
"Computer Science"
] | 2024-07-23T00:00:00 | https://arxiv.org/abs/2407.16375 | https://arxiv.org/pdf/2407.16375v1 | 2407.16375 | 10.1007/978-1-0716-4623-6_4 | 1 | 0 | true | https://github.com/haddocking/DeepRank-GNN-esm | null | 0.0753 |
60129d51a27615b5c98c7791bc9e01f6d1ab52f5e41e96925438816939b2a7ee | [
"arxiv",
"semantic_scholar"
] | GraphPrint: Extracting Features from 3D Protein Structure for Drug Target Affinity Prediction | Accurate drug target affinity prediction can improve drug candidate selection, accelerate the drug discovery process, and reduce drug production costs. Previous work focused on traditional fingerprints or used features extracted based on the amino acid sequence in the protein, ignoring its 3D structure which affects it... | [
"Amritpal Singh"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2024-07-15T00:00:00 | https://arxiv.org/abs/2407.10452 | https://arxiv.org/pdf/2407.10452v1 | 2407.10452 | 10.48550/arXiv.2407.10452 | 2 | 0 | false | null | arXiv.org | 0.1193 |
c18c774bacf89e06c3b19a948b2b1a22d36b14e914b03ef51d944679475bed08 | [
"arxiv",
"semantic_scholar"
] | Daisy: An integrated repeat protein curation service | Tandem repeats in proteins identification, classification and curation is a complex process that requires manual processing from experts, processing power and time. There are recent and relevant advances applying machine learning for protein structure prediction and repeat classification that are useful for this proces... | [
"Manuel Bezerra-Brandao",
"Ronaldo Romario Tunque Cahui",
"Layla Hirsh"
] | [
"cs.SE",
"cs.DB"
] | [
"Computer Science",
"Medicine"
] | 2024-07-10T00:00:00 | https://arxiv.org/abs/2407.07817 | https://arxiv.org/pdf/2407.07817v1 | 2407.07817 | 10.1016/j.jsb.2023.108033 | 0 | 0 | false | null | Journal of Structural Biology | 0 |
a3896f444ffa5a98ee968b2c8fedcee4d5532f0d25b2e834b405527c638a0b07 | [
"arxiv",
"semantic_scholar"
] | Dihedral Angle Adherence: Evaluating Protein Structure Predictions in the Absence of Experimental Data | Determining the 3D structures of proteins is essential in understanding their behavior in the cellular environment. Computational methods of predicting protein structures have advanced, but assessing prediction accuracy remains a challenge. The traditional method, RMSD, relies on experimentally determined structures an... | [
"Musa Azeem",
"Homayoun Valafar"
] | [
"q-bio.BM",
"cs.CE"
] | [
"Biology",
"Computer Science"
] | 2024-07-09T00:00:00 | https://arxiv.org/abs/2407.18336 | https://arxiv.org/pdf/2407.18336v1 | 2407.18336 | 10.48550/arXiv.2407.18336 | 0 | 0 | false | null | arXiv.org | 0 |
44ed8d6c79bbbcdbe40d9f13279f5e0f1b8fa285b8a5848c2e3452f58087b80b | [
"arxiv",
"semantic_scholar"
] | Improving AlphaFlow for Efficient Protein Ensembles Generation | Investigating conformational landscapes of proteins is a crucial way to understand their biological functions and properties. AlphaFlow stands out as a sequence-conditioned generative model that introduces flexibility into structure prediction models by fine-tuning AlphaFold under the flow-matching framework. Despite t... | [
"Shaoning Li",
"Mingyu Li",
"Yusong Wang",
"Xinheng He",
"Nanning Zheng",
"Jian Zhang",
"Pheng-Ann Heng"
] | [
"cs.LG",
"cs.AI",
"q-bio.QM"
] | [
"Computer Science",
"Biology"
] | 2024-07-08T00:00:00 | https://arxiv.org/abs/2407.12053 | https://arxiv.org/pdf/2407.12053v1 | 2407.12053 | 10.48550/arXiv.2407.12053 | 12 | 1 | false | null | arXiv.org | 0.2785 |
d6c6830be78d7dbfbc3d99e11f933fedbd8bc9dfd894f95e89db388a079bd5c2 | [
"arxiv",
"semantic_scholar"
] | Manipulating language models' training data to study syntactic constraint learning: the case of English passivization | Grammatical rules in natural languages are often characterized by exceptions. How do language learners learn these exceptions to otherwise general patterns? Here, we study this question through the case study of English passivization. While passivization is in general quite productive, there are cases where it cannot a... | [
"Cara Su-Yi Leong",
"Tal Linzen"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-07-05T00:00:00 | https://arxiv.org/abs/2407.04593 | https://arxiv.org/pdf/2407.04593v3 | 2407.04593 | null | 11 | 0 | false | null | null | 0.2698 |
3e0bbbe4273350a1b1dff04066f7614248bc2760c86e92bc075e5ee8e0f6d3d9 | [
"arxiv",
"semantic_scholar"
] | Open foundation models for Azerbaijani language | The emergence of multilingual large language models has enabled the development of language understanding and generation systems in Azerbaijani. However, most of the production-grade systems rely on cloud solutions, such as GPT-4. While there have been several attempts to develop open foundation models for Azerbaijani,... | [
"Jafar Isbarov",
"Kavsar Huseynova",
"Elvin Mammadov",
"Mammad Hajili",
"Duygu Ataman"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-07-02T00:00:00 | https://arxiv.org/abs/2407.02337 | https://arxiv.org/pdf/2407.02337v2 | 2407.02337 | 10.48550/arXiv.2407.02337 | 4 | 1 | true | null | null | 0.1747 |
e04788c820c9992ab035a8b08bf2becb131cecd0c320ac264e225b1cc0c06f65 | [
"arxiv",
"semantic_scholar"
] | Exploring Advanced Large Language Models with LLMsuite | This tutorial explores the advancements and challenges in the development of Large Language Models (LLMs) such as ChatGPT and Gemini. It addresses inherent limitations like temporal knowledge cutoffs, mathematical inaccuracies, and the generation of incorrect information, proposing solutions like Retrieval Augmented Ge... | [
"Giorgio Roffo"
] | [
"cs.CL",
"cs.CV"
] | [
"Computer Science"
] | 2024-07-01T00:00:00 | https://arxiv.org/abs/2407.12036 | https://arxiv.org/pdf/2407.12036v2 | 2407.12036 | 10.13140/RG.2.2.11774.80963 | 6 | 0 | true | null | arXiv.org | 0.2113 |
36675636f0e114873de41e718122d605bfb98b4d3e1150af8df25b5a36ceb1b9 | [
"arxiv",
"semantic_scholar"
] | DCI: An Accurate Quality Assessment Criteria for Protein Complex Structure Models | The structure of proteins is the basis for studying protein function and drug design. The emergence of AlphaFold 2 has greatly promoted the prediction of protein 3D structures, and it is of great significance to give an overall and accurate evaluation of the predicted models, especially the complex models. Among the ex... | [
"Wenda Wang",
"Jiaqi Zhai",
"He Huang",
"Xinqi Gong"
] | [
"q-bio.BM",
"math.OC"
] | [
"Biology",
"Mathematics"
] | 2024-06-30T00:00:00 | https://arxiv.org/abs/2407.00560 | https://arxiv.org/pdf/2407.00560v1 | 2407.00560 | null | 0 | 0 | false | null | null | 0 |
e47404aa57488ae73b9a1bb36219e8421a0655ed46edc2c12085808125de508f | [
"arxiv",
"semantic_scholar"
] | ProtSolM: Protein Solubility Prediction with Multi-modal Features | Understanding protein solubility is essential for their functional applications. Computational methods for predicting protein solubility are crucial for reducing experimental costs and enhancing the efficiency and success rates of protein engineering. Existing methods either construct a supervised learning scheme on sm... | [
"Yang Tan",
"Jia Zheng",
"Liang Hong",
"Bingxin Zhou"
] | [
"q-bio.QM"
] | [
"Biology",
"Computer Science"
] | 2024-06-28T00:00:00 | https://arxiv.org/abs/2406.19744 | https://arxiv.org/pdf/2406.19744v1 | 2406.19744 | 10.1109/BIBM62325.2024.10822310 | 19 | 1 | false | null | IEEE International Conference on Bioinformatics and Biomedicine | 0.3253 |
355c1431b3f4837cf02a6d170d62ef13c87af018dc192db0d6e01c92fcecf22c | [
"arxiv",
"semantic_scholar"
] | Accurate Prediction of Ligand-Protein Interaction Affinities with Fine-Tuned Small Language Models | We describe the accurate prediction of ligand-protein interaction (LPI) affinities, also known as drug-target interactions (DTI), with instruction fine-tuned pretrained generative small language models (SLMs). We achieved accurate predictions for a range of affinity values associated with ligand-protein interactions on... | [
"Ben Fauber"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2024-06-27T00:00:00 | https://arxiv.org/abs/2407.00111 | https://arxiv.org/pdf/2407.00111v1 | 2407.00111 | 10.48550/arXiv.2407.00111 | 2 | 0 | false | null | arXiv.org | 0.1193 |
196bc02db178f8c3c7c0932f529b00bab8ad6aa19c4f49e269cff9d4632d372f | [
"arxiv",
"semantic_scholar"
] | Evaluating representation learning on the protein structure universe | We introduce ProteinWorkshop, a comprehensive benchmark suite for representation learning on protein structures with Geometric Graph Neural Networks. We consider large-scale pre-training and downstream tasks on both experimental and predicted structures to enable the systematic evaluation of the quality of the learned ... | [
"Arian R. Jamasb",
"Alex Morehead",
"Chaitanya K. Joshi",
"Zuobai Zhang",
"Kieran Didi",
"Simon V. Mathis",
"Charles Harris",
"Jian Tang",
"Jianlin Cheng",
"Pietro Lio",
"Tom L. Blundell"
] | [
"cs.LG",
"q-bio.BM"
] | [
"Medicine",
"Computer Science",
"Biology"
] | 2024-06-19T00:00:00 | https://arxiv.org/abs/2406.13864 | https://arxiv.org/pdf/2406.13864v1 | 2406.13864 | 10.48550/arXiv.2406.13864 | 27 | 4 | true | null | International Conference on Learning Representations | 0.3618 |
9f42597c089362eae68e2ebbfc6a07517e1edacf5224f5c59225bb32ee1ff845 | [
"arxiv",
"semantic_scholar"
] | Evaluating Large Language Models along Dimensions of Language Variation: A Systematik Invesdigatiom uv Cross-lingual Generalization | While large language models exhibit certain cross-lingual generalization capabilities, they suffer from performance degradation (PD) on unseen closely-related languages (CRLs) and dialects relative to their high-resource language neighbour (HRLN). However, we currently lack a fundamental understanding of what kinds of ... | [
"Niyati Bafna",
"Kenton Murray",
"David Yarowsky"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-06-19T00:00:00 | https://arxiv.org/abs/2406.13718 | https://arxiv.org/pdf/2406.13718v2 | 2406.13718 | 10.48550/arXiv.2406.13718 | 8 | 0 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.2386 |
bfb628fbc9ae9b3728b634fe909f2f625981e680c653a8e6948e514a3bcf4870 | [
"arxiv",
"semantic_scholar"
] | FoldToken2: Learning compact, invariant and generative protein structure language | The equivalent nature of 3D coordinates has posed long term challenges in protein structure representation learning, alignment, and generation. Can we create a compact and invariant language that equivalently represents protein structures? Towards this goal, we propose FoldToken2 to transfer equivariant structures into... | [
"Zhangyang Gao",
"Cheng Tan",
"Stan Z. Li"
] | [
"q-bio.BM",
"cs.AI",
"cs.LG"
] | [
"Biology",
"Computer Science"
] | 2024-06-11T00:00:00 | https://arxiv.org/abs/2407.00050 | https://arxiv.org/pdf/2407.00050v1 | 2407.00050 | 10.1101/2024.06.11.598584 | 6 | 0 | false | null | bioRxiv | 0.2113 |
9c17e11331649c12fe84d71b53786165dacd92a8914d0ea2c762f5f0d517af05 | [
"arxiv",
"semantic_scholar"
] | A Fine-tuning Dataset and Benchmark for Large Language Models for Protein Understanding | The parallels between protein sequences and natural language in their sequential structures have inspired the application of large language models (LLMs) to protein understanding. Despite the success of LLMs in NLP, their effectiveness in comprehending protein sequences remains an open question, largely due to the abse... | [
"Yiqing Shen",
"Zan Chen",
"Michail Mamalakis",
"Luhan He",
"Haiyang Xia",
"Tianbin Li",
"Yanzhou Su",
"Junjun He",
"Yu Guang Wang"
] | [
"q-bio.QM",
"cs.AI",
"cs.CL",
"cs.LG"
] | [
"Computer Science",
"Biology"
] | 2024-06-08T00:00:00 | https://arxiv.org/abs/2406.05540 | https://arxiv.org/pdf/2406.05540v2 | 2406.05540 | 10.1109/BIBM62325.2024.10821894 | 30 | 3 | false | null | IEEE International Conference on Bioinformatics and Biomedicine | 0.3728 |
1b491df66514c78c2eb3dc8f7710cb8992db807cf7dbd1a673fc4008fa4c2a63 | [
"arxiv",
"semantic_scholar"
] | MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative Pre-Training | Multiple Sequence Alignment (MSA) plays a pivotal role in unveiling the evolutionary trajectories of protein families. The accuracy of protein structure predictions is often compromised for protein sequences that lack sufficient homologous information to construct high quality MSA. Although various methods have been pr... | [
"Bo Chen",
"Zhilei Bei",
"Xingyi Cheng",
"Pan Li",
"Jie Tang",
"Le Song"
] | [
"q-bio.BM",
"cs.AI",
"cs.LG"
] | [
"Biology",
"Computer Science"
] | 2024-06-08T00:00:00 | https://arxiv.org/abs/2406.05347 | https://arxiv.org/pdf/2406.05347v3 | 2406.05347 | 10.1101/2024.06.10.598380 | 17 | 2 | false | null | bioRxiv | 0.3138 |
d1241cbe57df01b11eb8ec808f8623bb8f89bdd1051acff41109bc2692bc0866 | [
"arxiv",
"semantic_scholar"
] | Benchmarking AlphaFold3's protein-protein complex accuracy and machine learning prediction reliability for binding free energy changes upon mutation | AlphaFold 3 (AF3), the latest version of protein structure prediction software, goes beyond its predecessors by predicting protein-protein complexes. It could revolutionize drug discovery and protein engineering, marking a major step towards comprehensive, automated protein structure prediction. However, independent va... | [
"JunJie Wee",
"Guo-Wei Wei"
] | [
"q-bio.BM",
"math.AT",
"q-bio.QM"
] | [
"Biology",
"Mathematics",
"Medicine"
] | 2024-06-06T00:00:00 | https://arxiv.org/abs/2406.03979 | https://arxiv.org/pdf/2406.03979v1 | 2406.03979 | null | 23 | 1 | false | null | arXiv.org | 0.3451 |
5684b3f2a90ed51a71b783a90d8f74a047b0320edef40eeba153078ee0de2e90 | [
"arxiv",
"semantic_scholar"
] | PediatricsGPT: Large Language Models as Chinese Medical Assistants for Pediatric Applications | Developing intelligent pediatric consultation systems offers promising prospects for improving diagnostic efficiency, especially in China, where healthcare resources are scarce. Despite recent advances in Large Language Models (LLMs) for Chinese medicine, their performance is sub-optimal in pediatric applications due t... | [
"Dingkang Yang",
"Jinjie Wei",
"Dongling Xiao",
"Shunli Wang",
"Tong Wu",
"Gang Li",
"Mingcheng Li",
"Shuaibing Wang",
"Jiawei Chen",
"Yue Jiang",
"Qingyao Xu",
"Ke Li",
"Peng Zhai",
"Lihua Zhang"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-05-29T00:00:00 | https://arxiv.org/abs/2405.19266 | https://arxiv.org/pdf/2405.19266v4 | 2405.19266 | 10.48550/arXiv.2405.19266 | 37 | 0 | true | null | Neural Information Processing Systems | 0.3949 |
eb05d23bacc9aca7ed631e50d6d3635a7a343bfe7ab81fe76e91876643eaf147 | [
"arxiv",
"semantic_scholar"
] | Boosting Protein Language Models with Negative Sample Mining | We introduce a pioneering methodology for boosting large language models in the domain of protein representation learning. Our primary contribution lies in the refinement process for correlating the over-reliance on co-evolution knowledge, in a way that networks are trained to distill invaluable insights from negative ... | [
"Yaoyao Xu",
"Xinjian Zhao",
"Xiaozhuang Song",
"Benyou Wang",
"Tianshu Yu"
] | [
"cs.AI",
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2024-05-28T00:00:00 | https://arxiv.org/abs/2405.17902 | https://arxiv.org/pdf/2405.17902v2 | 2405.17902 | 10.48550/arXiv.2405.17902 | 1 | 0 | false | null | null | 0.0753 |
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