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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
a0b7cc577019d1575ad631664c31136505b36d6e08d9c62369f9516a5113e6e7 | [
"arxiv",
"semantic_scholar"
] | PatchProt: Hydrophobic patch prediction using protein foundation models | Hydrophobic patches on protein surfaces play important functional roles in protein-protein and protein-ligand interactions. Large hydrophobic surfaces are also involved in the progression of aggregation diseases. Predicting exposed hydrophobic patches from a protein sequence has been shown to be a difficult task. Fine-... | [
"Dea Gogishvili",
"Emmanuel Minois-Genin",
"Jan van Eck",
"Sanne Abeln"
] | [
"q-bio.QM",
"cs.AI",
"cs.LG"
] | [
"Biology",
"Computer Science",
"Medicine"
] | 2024-05-24T00:00:00 | https://arxiv.org/abs/2405.15928 | https://arxiv.org/pdf/2405.15928v1 | 2405.15928 | 10.1093/bioadv/vbae154 | 6 | 0 | false | null | Bioinformatics Advances | 0.2113 |
70122fc9f0338bf385215485ac3fa710996065401d7bd83abdab3517d8eb2ba0 | [
"arxiv",
"semantic_scholar"
] | Learning the Language of Protein Structure | Representation learning and \emph{de novo} generation of proteins are pivotal computational biology tasks. Whilst natural language processing (NLP) techniques have proven highly effective for protein sequence modelling, structure modelling presents a complex challenge, primarily due to its continuous and three-dimensio... | [
"Benoit Gaujac",
"Jérémie Donà",
"Liviu Copoiu",
"Timothy Atkinson",
"Thomas Pierrot",
"Thomas D. Barrett"
] | [
"q-bio.QM",
"cs.LG"
] | [
"Computer Science",
"Biology"
] | 2024-05-24T00:00:00 | https://arxiv.org/abs/2405.15840 | https://arxiv.org/pdf/2405.15840v2 | 2405.15840 | 10.48550/arXiv.2405.15840 | 17 | 1 | false | null | null | 0.3138 |
71d2f676af919902dbd3ee3ab6e0affdfbe5ab43b26320eb6033f12732353e22 | [
"arxiv",
"semantic_scholar"
] | Babysit A Language Model From Scratch: Interactive Language Learning by Trials and Demonstrations | Humans are efficient language learners and inherently social creatures. Our language development is largely shaped by our social interactions, for example, the demonstration and feedback from caregivers. Contrary to human language learning, recent advancements in large language models have primarily adopted a non-inter... | [
"Ziqiao Ma",
"Zekun Wang",
"Joyce Chai"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-05-22T00:00:00 | https://arxiv.org/abs/2405.13828 | https://arxiv.org/pdf/2405.13828v2 | 2405.13828 | 10.48550/arXiv.2405.13828 | 14 | 0 | false | null | North American Chapter of the Association for Computational Linguistics | 0.294 |
3ae1698ce8c0a871547135b6242855a4ebb9129b0fe0085882bba33e307ab8c3 | [
"arxiv",
"semantic_scholar"
] | Identifying the minimal sets of distance restraints for FRET-assisted protein structural modeling | Proteins naturally occur in crowded cellular environments and interact with other proteins, nucleic acids, and organelles. Since most previous experimental protein structure determination techniques require that proteins occur in idealized, non-physiological environments, the effects of realistic cellular environments ... | [
"Zhuoyi Liu",
"Alex T. Grigas",
"Jacob Sumner",
"Edward Knab",
"Caitlin M. Davis",
"Corey S. O'Hern"
] | [
"physics.bio-ph",
"q-bio.BM"
] | [
"Physics",
"Biology",
"Medicine"
] | 2024-05-13T00:00:00 | https://arxiv.org/abs/2405.07983 | https://arxiv.org/pdf/2405.07983v2 | 2405.07983 | 10.1002/pro.5219 | 0 | 0 | false | null | Protein Science | 0 |
27aa4f17a5d75f6efa1677455377ada76e922dcabfe586039b56b09597bef78d | [
"arxiv",
"semantic_scholar"
] | Fine-tuning Protein Language Models with Deep Mutational Scanning improves Variant Effect Prediction | Protein Language Models (PLMs) have emerged as performant and scalable tools for predicting the functional impact and clinical significance of protein-coding variants, but they still lag experimental accuracy. Here, we present a novel fine-tuning approach to improve the performance of PLMs with experimental maps of var... | [
"Aleix Lafita",
"Ferran Gonzalez",
"Mahmoud Hossam",
"Paul Smyth",
"Jacob Deasy",
"Ari Allyn-Feuer",
"Daniel Seaton",
"Stephen Young"
] | [
"q-bio.GN",
"cs.LG"
] | [
"Computer Science",
"Biology"
] | 2024-05-10T00:00:00 | https://arxiv.org/abs/2405.06729 | https://arxiv.org/pdf/2405.06729v1 | 2405.06729 | 10.48550/arXiv.2405.06729 | 16 | 2 | false | null | arXiv.org | 0.3076 |
e8e9616cc1ca90d9351412243e1e3a9787ea28c3e7130bdb0c637635c1158e0c | [
"arxiv",
"semantic_scholar"
] | Protein binding affinity prediction under multiple substitutions applying eGNNs on Residue and Atomic graphs combined with Language model information: eGRAL | Protein-protein interactions (PPIs) play a crucial role in numerous biological processes. Developing methods that predict binding affinity changes under substitution mutations is fundamental for modelling and re-engineering biological systems. Deep learning is increasingly recognized as a powerful tool capable of bridg... | [
"Arturo Fiorellini-Bernardis",
"Sebastien Boyer",
"Christoph Brunken",
"Bakary Diallo",
"Karim Beguir",
"Nicolas Lopez-Carranza",
"Oliver Bent"
] | [
"q-bio.QM",
"cs.AI",
"cs.LG"
] | [
"Biology",
"Computer Science"
] | 2024-05-03T00:00:00 | https://arxiv.org/abs/2405.02374 | https://arxiv.org/pdf/2405.02374v1 | 2405.02374 | 10.48550/arXiv.2405.02374 | 1 | 0 | false | null | arXiv.org | 0.0753 |
bd9fa1e533ac60d118af2ff6e4816192fd52a4d2e0bca7b7b99b1e5c7671df31 | [
"arxiv",
"semantic_scholar"
] | Detection of circular permutations by Protein Language Models | Protein circular permutations are crucial for understanding protein evolution and functionality. Traditional detection methods, sequence-based or structure-based, struggle with accuracy and computational efficiency, the latter also limited by treating proteins as rigid bodies. The plmCP method, utilizing a protein lang... | [
"Yue Hu",
"Bin Huang",
"Chunzi Zang"
] | [
"q-bio.QM"
] | [
"Medicine",
"Biology"
] | 2024-04-23T00:00:00 | https://arxiv.org/abs/2404.15087 | https://arxiv.org/pdf/2404.15087v2 | 2404.15087 | 10.1016/j.csbj.2024.12.029 | 1 | 0 | false | null | Computational and Structural Biotechnology Journal | 0.0753 |
3178545dea9826a95d7ef4b463c338df7ae9d07165c419654c9276ae6c49d870 | [
"arxiv",
"semantic_scholar"
] | Simple, Efficient and Scalable Structure-aware Adapter Boosts Protein Language Models | Fine-tuning Pre-trained protein language models (PLMs) has emerged as a prominent strategy for enhancing downstream prediction tasks, often outperforming traditional supervised learning approaches. As a widely applied powerful technique in natural language processing, employing Parameter-Efficient Fine-Tuning technique... | [
"Yang Tan",
"Mingchen Li",
"Bingxin Zhou",
"Bozitao Zhong",
"Lirong Zheng",
"Pan Tan",
"Ziyi Zhou",
"Huiqun Yu",
"Guisheng Fan",
"Liang Hong"
] | [
"cs.CL",
"cs.LG",
"q-bio.BM"
] | [
"Computer Science",
"Biology",
"Medicine"
] | 2024-04-23T00:00:00 | https://arxiv.org/abs/2404.14850 | https://arxiv.org/pdf/2404.14850v1 | 2404.14850 | 10.48550/arXiv.2404.14850 | 21 | 0 | true | https://github.com/tyang816/SES-Adapter | Journal of Chemical Information and Modeling | 0.3356 |
82bac94f1ebc01f0031e52a53e4916ee4a3feb7c0e0d19033e465a62b854845d | [
"arxiv",
"semantic_scholar"
] | ScaleFold: Reducing AlphaFold Initial Training Time to 10 Hours | AlphaFold2 has been hailed as a breakthrough in protein folding. It can rapidly predict protein structures with lab-grade accuracy. However, its implementation does not include the necessary training code. OpenFold is the first trainable public reimplementation of AlphaFold. AlphaFold training procedure is prohibitivel... | [
"Feiwen Zhu",
"Arkadiusz Nowaczynski",
"Rundong Li",
"Jie Xin",
"Yifei Song",
"Michal Marcinkiewicz",
"Sukru Burc Eryilmaz",
"Jun Yang",
"Michael Andersch"
] | [
"cs.LG",
"cs.AI",
"cs.DC",
"q-bio.QM"
] | [
"Computer Science",
"Biology"
] | 2024-04-17T00:00:00 | https://arxiv.org/abs/2404.11068 | https://arxiv.org/pdf/2404.11068v1 | 2404.11068 | 10.1145/3649329.3657326 | 11 | 0 | false | null | Design Automation Conference | 0.2698 |
2e66784743ed2c15fd4452170a70c0787c82369075458d2d53884a6d81f0c93e | [
"arxiv",
"semantic_scholar"
] | HelixFold-Multimer: Elevating Protein Complex Structure Prediction to New Heights | While monomer protein structure prediction tools boast impressive accuracy, the prediction of protein complex structures remains a daunting challenge in the field. This challenge is particularly pronounced in scenarios involving complexes with protein chains from different species, such as antigen-antibody interactions... | [
"Xiaomin Fang",
"Jie Gao",
"Jing Hu",
"Lihang Liu",
"Yang Xue",
"Xiaonan Zhang",
"Kunrui Zhu"
] | [
"q-bio.BM",
"cs.AI"
] | [
"Computer Science",
"Biology"
] | 2024-04-16T00:00:00 | https://arxiv.org/abs/2404.10260 | https://arxiv.org/pdf/2404.10260v2 | 2404.10260 | 10.48550/arXiv.2404.10260 | 14 | 1 | false | null | arXiv.org | 0.294 |
ea062e2411156c0d3caa22f40dafc93738cd42fbcaf81582356fd02b79a16076 | [
"arxiv",
"semantic_scholar"
] | PRODIS -- a speech database and a phoneme-based language model for the study of predictability effects in Polish | We present a speech database and a phoneme-level language model of Polish. The database and model are designed for the analysis of prosodic and discourse factors and their impact on acoustic parameters in interaction with predictability effects. The database is also the first large, publicly available Polish speech cor... | [
"Zofia Malisz",
"Jan Foremski",
"Małgorzata Kul"
] | [
"cs.CL",
"cs.SD",
"eess.AS"
] | [
"Computer Science",
"Engineering"
] | 2024-04-15T00:00:00 | https://arxiv.org/abs/2404.10112 | https://arxiv.org/pdf/2404.10112v1 | 2404.10112 | 10.48550/arXiv.2404.10112 | 1 | 0 | false | null | International Conference on Language Resources and Evaluation | 0.0753 |
12bb9962964829e2930b933f77322bdfffdc9979d32f6509b3d9ba2fefb95c37 | [
"arxiv",
"semantic_scholar"
] | Auxiliary task demands mask the capabilities of smaller language models | Developmental psychologists have argued about when cognitive capacities such as language understanding or theory of mind emerge. These debates often hinge on the concept of "task demands" -- the auxiliary challenges associated with performing a particular evaluation -- that may mask the child's underlying ability. The ... | [
"Jennifer Hu",
"Michael C. Frank"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-04-03T00:00:00 | https://arxiv.org/abs/2404.02418 | https://arxiv.org/pdf/2404.02418v2 | 2404.02418 | 10.48550/arXiv.2404.02418 | 62 | 3 | false | null | arXiv.org | 0.4498 |
66acb8541b33578b36663425d587945f09653793619d871d74cc701ba2bb2bba | [
"arxiv",
"semantic_scholar"
] | ProLLM: Protein Chain-of-Thoughts Enhanced LLM for Protein-Protein Interaction Prediction | The prediction of protein-protein interactions (PPIs) is crucial for understanding biological functions and diseases. Previous machine learning approaches to PPI prediction mainly focus on direct physical interactions, ignoring the broader context of nonphysical connections through intermediate proteins, thus limiting ... | [
"Mingyu Jin",
"Haochen Xue",
"Zhenting Wang",
"Boming Kang",
"Ruosong Ye",
"Kaixiong Zhou",
"Mengnan Du",
"Yongfeng Zhang"
] | [
"q-bio.BM",
"cs.LG",
"q-bio.MN"
] | [
"Biology",
"Computer Science"
] | 2024-03-30T00:00:00 | https://arxiv.org/abs/2405.06649 | https://arxiv.org/pdf/2405.06649v2 | 2405.06649 | 10.1101/2024.04.18.590025 | 35 | 0 | true | https://github.com/MingyuJ666/ProLLM | bioRxiv | 0.3891 |
e7a4c05fe267c4a25bd9ae4c2c68e3a040840bc2e647b6cafb71fa1342ac3e93 | [
"arxiv",
"semantic_scholar"
] | IDP-Bert: Predicting Properties of Intrinsically Disordered Proteins (IDP) Using Large Language Models | Intrinsically Disordered Proteins (IDPs) constitute a large and structure-less class of proteins with significant functions. The existence of IDPs challenges the conventional notion that the biological functions of proteins rely on their three-dimensional structures. Despite lacking well-defined spatial arrangements, t... | [
"Parisa Mollaei",
"Danush Sadasivam",
"Chakradhar Guntuboina",
"Amir Barati Farimani"
] | [
"q-bio.BM"
] | [
"Medicine",
"Biology"
] | 2024-03-28T00:00:00 | https://arxiv.org/abs/2403.19762 | https://arxiv.org/pdf/2403.19762v2 | 2403.19762 | 10.1021/acs.jpcb.4c02507 | 10 | 0 | false | null | Journal of Physical Chemistry B | 0.2603 |
79e2e96fd884323e4fb0ed5426f8afc7b716b92f7a54400662b72aef932b8b45 | [
"arxiv",
"semantic_scholar"
] | Are Compressed Language Models Less Subgroup Robust? | To reduce the inference cost of large language models, model compression is increasingly used to create smaller scalable models. However, little is known about their robustness to minority subgroups defined by the labels and attributes of a dataset. In this paper, we investigate the effects of 18 different compression ... | [
"Leonidas Gee",
"Andrea Zugarini",
"Novi Quadrianto"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2024-03-26T00:00:00 | https://arxiv.org/abs/2403.17811 | https://arxiv.org/pdf/2403.17811v1 | 2403.17811 | 10.18653/v1/2023.emnlp-main.983 | 2 | 0 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.1193 |
f13a3031b7e1b19d233f29125e0d9d81ff088cb8330048b9c0dd1024e2bf25a7 | [
"arxiv",
"semantic_scholar"
] | Emergent World Models and Latent Variable Estimation in Chess-Playing Language Models | Language models have shown unprecedented capabilities, sparking debate over the source of their performance. Is it merely the outcome of learning syntactic patterns and surface level statistics, or do they extract semantics and a world model from the text? Prior work by Li et al. investigated this by training a GPT mod... | [
"Adam Karvonen"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2024-03-21T00:00:00 | https://arxiv.org/abs/2403.15498 | https://arxiv.org/pdf/2403.15498v2 | 2403.15498 | 10.48550/arXiv.2403.15498 | 59 | 2 | false | null | arXiv.org | 0.4445 |
5cf25741665611e22072be9eb97db4afa25dc5927046a6b41539586549068b00 | [
"arxiv",
"semantic_scholar"
] | Clinical information extraction for Low-resource languages with Few-shot learning using Pre-trained language models and Prompting | Automatic extraction of medical information from clinical documents poses several challenges: high costs of required clinical expertise, limited interpretability of model predictions, restricted computational resources and privacy regulations. Recent advances in domain-adaptation and prompting methods showed promising ... | [
"Phillip Richter-Pechanski",
"Philipp Wiesenbach",
"Dominic M. Schwab",
"Christina Kiriakou",
"Nicolas Geis",
"Christoph Dieterich",
"Anette Frank"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2024-03-20T00:00:00 | https://arxiv.org/abs/2403.13369 | https://arxiv.org/pdf/2403.13369v2 | 2403.13369 | 10.1017/nlp.2024.52 | 12 | 0 | false | null | Natural Language Processing | 0.2785 |
5906bbc9c25873cbc482ed652fd00b99c63697c63b94defd62768fe697692e1d | [
"arxiv",
"semantic_scholar"
] | Document Author Classification Using Parsed Language Structure | Over the years there has been ongoing interest in detecting authorship of a text based on statistical properties of the text, such as by using occurrence rates of noncontextual words. In previous work, these techniques have been used, for example, to determine authorship of all of \emph{The Federalist Papers}. Such met... | [
"Todd K Moon",
"Jacob H. Gunther"
] | [
"cs.CL",
"eess.AS"
] | [
"Computer Science",
"Engineering"
] | 2024-03-20T00:00:00 | https://arxiv.org/abs/2403.13253 | https://arxiv.org/pdf/2403.13253v1 | 2403.13253 | 10.5121/ijnlc.2024.13104 | 0 | 0 | false | null | International Journal on Natural Language Computing | 0 |
85e0b9ee143f1fcc602843116f647c2421c54521c54cbd63c84b4e8d608feda1 | [
"arxiv",
"semantic_scholar"
] | Pragmatic Competence Evaluation of Large Language Models for the Korean Language | Benchmarks play a significant role in the current evaluation of Large Language Models (LLMs), yet they often overlook the models' abilities to capture the nuances of human language, primarily focusing on evaluating embedded knowledge and technical skills. To address this gap, our study evaluates how well LLMs understan... | [
"Dojun Park",
"Jiwoo Lee",
"Hyeyun Jeong",
"Seohyun Park",
"Sungeun Lee"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-03-19T00:00:00 | https://arxiv.org/abs/2403.12675 | https://arxiv.org/pdf/2403.12675v2 | 2403.12675 | null | 9 | 0 | false | null | Pacific Asia Conference on Language, Information and Computation | 0.25 |
c308ff793346730f430bd9bb7891dc496dbe4dd1d152b39a6e64dc671afd1e2a | [
"arxiv",
"semantic_scholar"
] | Language Evolution with Deep Learning | Computational modeling plays an essential role in the study of language emergence. It aims to simulate the conditions and learning processes that could trigger the emergence of a structured language within a simulated controlled environment. Several methods have been used to investigate the origin of our language, incl... | [
"Mathieu Rita",
"Paul Michel",
"Rahma Chaabouni",
"Olivier Pietquin",
"Emmanuel Dupoux",
"Florian Strub"
] | [
"cs.CL",
"cs.MA"
] | [
"Computer Science"
] | 2024-03-18T00:00:00 | https://arxiv.org/abs/2403.11958 | https://arxiv.org/pdf/2403.11958v1 | 2403.11958 | 10.48550/arXiv.2403.11958 | 4 | 0 | false | null | arXiv.org | 0.1747 |
23bf9dbb62f19e2e7d79222f2d0a38475c004cd7f6e6c33bc1d7527e99668ef4 | [
"arxiv",
"semantic_scholar"
] | On Recovering Higher-order Interactions from Protein Language Models | Protein language models leverage evolutionary information to perform state-of-the-art 3D structure and zero-shot variant prediction. Yet, extracting and explaining all the mutational interactions that govern model predictions remains difficult as it requires querying the entire amino acid space for $n$ sites using $20^... | [
"Darin Tsui",
"Amirali Aghazadeh"
] | [
"q-bio.BM",
"cs.AI",
"cs.LG"
] | [
"Biology",
"Computer Science"
] | 2024-03-15T00:00:00 | https://arxiv.org/abs/2405.06645 | https://arxiv.org/pdf/2405.06645v1 | 2405.06645 | 10.48550/arXiv.2405.06645 | 9 | 0 | true | https://github.com/amirgroup-codes/InteractionRecovery | arXiv.org | 0.25 |
81d8ac72001a2f9a2b9ff6141d4704d9d34de13762fb0325af67c9facc5e2fe9 | [
"arxiv",
"semantic_scholar"
] | Diffusion on language model encodings for protein sequence generation | Protein sequence design has seen significant advances through discrete diffusion and autoregressive approaches, yet the potential of continuous diffusion remains underexplored. Here, we present DiMA, a latent diffusion framework that operates on protein language model representations. Through systematic exploration of ... | [
"Viacheslav Meshchaninov",
"Pavel Strashnov",
"Andrey Shevtsov",
"Fedor Nikolaev",
"Nikita Ivanisenko",
"Olga Kardymon",
"Dmitry Vetrov"
] | [
"cs.LG",
"cs.AI",
"q-bio.BM"
] | [
"Computer Science",
"Biology"
] | 2024-03-06T00:00:00 | https://arxiv.org/abs/2403.03726 | https://arxiv.org/pdf/2403.03726v4 | 2403.03726 | null | 25 | 1 | true | https://github.com/MeshchaninovViacheslav/DiMA}{GitHub} | International Conference on Machine Learning | 0.3537 |
627b133b064603a539be91a0e27517549ec47db60c456d6c79068192dd878eae | [
"arxiv",
"semantic_scholar"
] | ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modeling | Protein language models have demonstrated significant potential in the field of protein engineering. However, current protein language models primarily operate at the residue scale, which limits their ability to provide information at the atom level. This limitation prevents us from fully exploiting the capabilities of... | [
"Kangjie Zheng",
"Siyu Long",
"Tianyu Lu",
"Junwei Yang",
"Xinyu Dai",
"Ming Zhang",
"Zaiqing Nie",
"Wei-Ying Ma",
"Hao Zhou"
] | [
"q-bio.BM",
"cs.CE",
"cs.LG"
] | [
"Biology",
"Computer Science"
] | 2024-03-05T00:00:00 | https://arxiv.org/abs/2403.12995 | https://arxiv.org/pdf/2403.12995v4 | 2403.12995 | 10.1101/2024.03.04.583284 | 19 | 1 | true | https://github.com/zhengkangjie/ESM-AA | bioRxiv | 0.3253 |
16fe3c2c6abb50260051879d07d2e2f84b992b27d92b039395736072129baf9c | [
"arxiv",
"semantic_scholar"
] | A Protein Structure Prediction Approach Leveraging Transformer and CNN Integration | Proteins are essential for life, and their structure determines their function. The protein secondary structure is formed by the folding of the protein primary structure, and the protein tertiary structure is formed by the bending and folding of the secondary structure. Therefore, the study of protein secondary structu... | [
"Yanlin Zhou",
"Kai Tan",
"Xinyu Shen",
"Zheng He",
"Haotian Zheng"
] | [
"q-bio.BM",
"cs.LG"
] | [
"Computer Science",
"Biology"
] | 2024-02-29T00:00:00 | https://arxiv.org/abs/2402.19095 | https://arxiv.org/pdf/2402.19095v2 | 2402.19095 | 10.1109/ICAACE61206.2024.10548253 | 17 | 0 | false | null | null | 0.3138 |
8566277431df6c4de2e60b89887ee89d0def38c46ca1933ed95fa6d808171f0f | [
"arxiv",
"semantic_scholar"
] | Protein Multimer Structure Prediction via Prompt Learning | Understanding the 3D structures of protein multimers is crucial, as they play a vital role in regulating various cellular processes. It has been empirically confirmed that the multimer structure prediction~(MSP) can be well handled in a step-wise assembly fashion using provided dimer structures and predicted protein-pr... | [
"Ziqi Gao",
"Xiangguo Sun",
"Zijing Liu",
"Yu Li",
"Hong Cheng",
"Jia Li"
] | [
"cs.CE"
] | [
"Computer Science"
] | 2024-02-29T00:00:00 | https://arxiv.org/abs/2402.18813 | https://arxiv.org/pdf/2402.18813v1 | 2402.18813 | 10.48550/arXiv.2402.18813 | 16 | 1 | true | https://github.com/zqgao22/PromptMSP} | International Conference on Learning Representations | 0.3076 |
f7ed9611db3d0e691778ac27b501a598a3db0c1ab01301849aecc792b84bbdcf | [
"arxiv",
"semantic_scholar"
] | ProtLLM: An Interleaved Protein-Language LLM with Protein-as-Word Pre-Training | We propose ProtLLM, a versatile cross-modal large language model (LLM) for both protein-centric and protein-language tasks. ProtLLM features a unique dynamic protein mounting mechanism, enabling it to handle complex inputs where the natural language text is interspersed with an arbitrary number of proteins. Besides, we... | [
"Le Zhuo",
"Zewen Chi",
"Minghao Xu",
"Heyan Huang",
"Heqi Zheng",
"Conghui He",
"Xian-Ling Mao",
"Wentao Zhang"
] | [
"q-bio.BM",
"cs.AI",
"cs.CL",
"cs.LG"
] | [
"Computer Science",
"Biology"
] | 2024-02-28T00:00:00 | https://arxiv.org/abs/2403.07920 | https://arxiv.org/pdf/2403.07920v1 | 2403.07920 | 10.48550/arXiv.2403.07920 | 29 | 3 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.3693 |
1db93edbc47a3acd607b4e620e329fa4ed25c4c0dc0f48470727c205b67da618 | [
"arxiv",
"semantic_scholar"
] | Diffusion Language Models Are Versatile Protein Learners | This paper introduces diffusion protein language model (DPLM), a versatile protein language model that demonstrates strong generative and predictive capabilities for protein sequences. We first pre-train scalable DPLMs from evolutionary-scale protein sequences within a generative self-supervised discrete diffusion prob... | [
"Xinyou Wang",
"Zaixiang Zheng",
"Fei Ye",
"Dongyu Xue",
"Shujian Huang",
"Quanquan Gu"
] | [
"cs.LG",
"q-bio.BM"
] | [
"Computer Science",
"Biology"
] | 2024-02-28T00:00:00 | https://arxiv.org/abs/2402.18567 | https://arxiv.org/pdf/2402.18567v2 | 2402.18567 | 10.48550/arXiv.2402.18567 | 130 | 15 | true | https://github.com/bytedance/dplm} | International Conference on Machine Learning | 0.6021 |
2d361a6742ab3b6880d0db2962ed86fa6848d91092645eb75a088e08a9a77af2 | [
"arxiv",
"semantic_scholar"
] | ProLLaMA: A Protein Large Language Model for Multi-Task Protein Language Processing | Recent advances in Protein Language Models (PLMs) have transformed protein engineering, yet unlike their counterparts in Natural Language Processing (NLP), current PLMs exhibit a fundamental limitation: they excel in either Protein Language Understanding (PLU) or Protein Language Generation (PLG), but rarely both. This... | [
"Liuzhenghao Lv",
"Zongying Lin",
"Hao Li",
"Yuyang Liu",
"Jiaxi Cui",
"Calvin Yu-Chian Chen",
"Li Yuan",
"Yonghong Tian"
] | [
"cs.CE",
"q-bio.BM"
] | [
"Computer Science",
"Biology"
] | 2024-02-26T00:00:00 | https://arxiv.org/abs/2402.16445 | https://arxiv.org/pdf/2402.16445v3 | 2402.16445 | 10.1109/TAI.2025.3564914 | 89 | 10 | true | https://github.com/PKU-YuanGroup/ProLLaMA | IEEE Transactions on Artificial Intelligence | 0.5207 |
4b3fa5ac2b12de8e3fe36b67a29a76eac458045ee52277a5a15fbff825a5860a | [
"arxiv",
"semantic_scholar"
] | How Important Is Tokenization in French Medical Masked Language Models? | Subword tokenization has become the prevailing standard in the field of natural language processing (NLP) over recent years, primarily due to the widespread utilization of pre-trained language models. This shift began with Byte-Pair Encoding (BPE) and was later followed by the adoption of SentencePiece and WordPiece. W... | [
"Yanis Labrak",
"Adrien Bazoge",
"Beatrice Daille",
"Mickael Rouvier",
"Richard Dufour"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2024-02-22T00:00:00 | https://arxiv.org/abs/2402.15010 | https://arxiv.org/pdf/2402.15010v2 | 2402.15010 | 10.48550/arXiv.2402.15010 | 2 | 0 | false | null | International Conference on Language Resources and Evaluation | 0.1193 |
cd6a8a9ee40166a4dba02d9f168ca0a5b9b5acd51793aa42d5108a5cb53cd484 | [
"arxiv",
"semantic_scholar"
] | Analysing The Impact of Sequence Composition on Language Model Pre-Training | Most language model pre-training frameworks concatenate multiple documents into fixed-length sequences and use causal masking to compute the likelihood of each token given its context; this strategy is widely adopted due to its simplicity and efficiency. However, to this day, the influence of the pre-training sequence ... | [
"Yu Zhao",
"Yuanbin Qu",
"Konrad Staniszewski",
"Szymon Tworkowski",
"Wei Liu",
"Piotr Miłoś",
"Yuxiang Wu",
"Pasquale Minervini"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-02-21T00:00:00 | https://arxiv.org/abs/2402.13991 | https://arxiv.org/pdf/2402.13991v1 | 2402.13991 | 10.18653/v1/2024.acl-long.427 | 23 | 2 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.3451 |
4f29fe266275011a800fbb298a64913404ac6356e54a4aebabdf381b53238deb | [
"arxiv",
"semantic_scholar"
] | TEXT2AFFORD: Probing Object Affordance Prediction abilities of Language Models solely from Text | We investigate the knowledge of object affordances in pre-trained language models (LMs) and pre-trained Vision-Language models (VLMs). A growing body of literature shows that PTLMs fail inconsistently and non-intuitively, demonstrating a lack of reasoning and grounding. To take a first step toward quantifying the effec... | [
"Sayantan Adak",
"Daivik Agrawal",
"Animesh Mukherjee",
"Somak Aditya"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-02-20T00:00:00 | https://arxiv.org/abs/2402.12881 | https://arxiv.org/pdf/2402.12881v3 | 2402.12881 | 10.18653/v1/2024.conll-1.27 | 7 | 0 | true | https://github.com/sayantan11995/Text2Afford | Conference on Computational Natural Language Learning | 0.2258 |
97aa69029586ee3c37de4bdabc558289466e08f5b3c14f62f8e8e5e2bb8e8d51 | [
"arxiv",
"semantic_scholar"
] | Exploring the Frontier of Vision-Language Models: A Survey of Current Methodologies and Future Directions | The advent of Large Language Models (LLMs) has significantly reshaped the trajectory of the AI revolution. Nevertheless, these LLMs exhibit a notable limitation, as they are primarily adept at processing textual information. To address this constraint, researchers have endeavored to integrate visual capabilities with L... | [
"Akash Ghosh",
"Arkadeep Acharya",
"Sriparna Saha",
"Vinija Jain",
"Aman Chadha"
] | [
"cs.CV",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2024-02-20T00:00:00 | https://arxiv.org/abs/2404.07214 | https://arxiv.org/pdf/2404.07214v4 | 2404.07214 | 10.48550/arXiv.2404.07214 | 85 | 3 | false | null | arXiv.org | 0.4836 |
c2e884c616d99c0b45de7f42f49d7c491805537ba6d607e924412c2438ec1a06 | [
"arxiv",
"semantic_scholar"
] | Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents | Large language models (LLMs) have achieved success in acting as agents, which interact with environments through tools such as search engines. However, LLMs are optimized for language generation instead of tool use during training or alignment, limiting their effectiveness as agents. To resolve this problem, previous w... | [
"Renxi Wang",
"Haonan Li",
"Xudong Han",
"Yixuan Zhang",
"Timothy Baldwin"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-02-18T00:00:00 | https://arxiv.org/abs/2402.11651 | https://arxiv.org/pdf/2402.11651v2 | 2402.11651 | 10.48550/arXiv.2402.11651 | 46 | 4 | false | null | arXiv.org | 0.418 |
3a2b4d363f14a51266e4ed10929e95c205685227469ebebdb852655f7bbbe91d | [
"arxiv",
"semantic_scholar"
] | Fast Vocabulary Transfer for Language Model Compression | Real-world business applications require a trade-off between language model performance and size. We propose a new method for model compression that relies on vocabulary transfer. We evaluate the method on various vertical domains and downstream tasks. Our results indicate that vocabulary transfer can be effectively us... | [
"Leonidas Gee",
"Andrea Zugarini",
"Leonardo Rigutini",
"Paolo Torroni"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2024-02-15T00:00:00 | https://arxiv.org/abs/2402.09977 | https://arxiv.org/pdf/2402.09977v1 | 2402.09977 | 10.18653/v1/2022.emnlp-industry.41 | 50 | 10 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.5207 |
09101d37ca0bf2bb7fcbd8d414984aefb5842198af722f0c99dd9890c458ac97 | [
"arxiv",
"semantic_scholar"
] | ProtChatGPT: Towards Understanding Proteins with Large Language Models | Protein research is crucial in various fundamental disciplines, but understanding their intricate structure-function relationships remains challenging. Recent Large Language Models (LLMs) have made significant strides in comprehending task-specific knowledge, suggesting the potential for ChatGPT-like systems specialize... | [
"Chao Wang",
"Hehe Fan",
"Ruijie Quan",
"Yi Yang"
] | [
"cs.CE",
"cs.AI",
"q-bio.BM"
] | [
"Computer Science",
"Biology"
] | 2024-02-15T00:00:00 | https://arxiv.org/abs/2402.09649 | https://arxiv.org/pdf/2402.09649v2 | 2402.09649 | 10.48550/arXiv.2402.09649 | 27 | 1 | false | null | arXiv.org | 0.3618 |
af4a70d73d6e73598363457efb55c1a5c0a988b1c5b1c542c05311e5cad1a24a | [
"arxiv",
"semantic_scholar"
] | Structured Language Generation Model: Loss Calibration and Formatted Decoding for Robust Structure Prediction and Knowledge Retrieval | Modern generative pre-trained language models excel at open-ended text generation, yet continue to underperform on structure-related tasks such as NER, relation extraction, and semantic role labeling, especially when compared to encoder-only models of similar sizes. While this gap has been attributed to limited structu... | [
"Minho Lee",
"Junghyun Min",
"Yerang Kim",
"Woochul Lee",
"Yeonsoo Lee"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-02-14T00:00:00 | https://arxiv.org/abs/2402.08971 | https://arxiv.org/pdf/2402.08971v3 | 2402.08971 | null | 2 | 0 | false | null | null | 0.1193 |
0be0aad374a66a9f6877747bee25bb60c6bb085669c69dbea7c4edb89a9bdeac | [
"arxiv",
"semantic_scholar"
] | PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for Efficient and Generalizable Compound-Protein Interaction Prediction | Compound-Protein Interaction (CPI) prediction aims to predict the pattern and strength of compound-protein interactions for rational drug discovery. Existing deep learning-based methods utilize only the single modality of protein sequences or structures and lack the co-modeling of the joint distribution of the two moda... | [
"Lirong Wu",
"Yufei Huang",
"Cheng Tan",
"Zhangyang Gao",
"Bozhen Hu",
"Haitao Lin",
"Zicheng Liu",
"Stan Z. Li"
] | [
"q-bio.BM",
"cs.AI",
"cs.LG"
] | [
"Computer Science",
"Biology"
] | 2024-02-13T00:00:00 | https://arxiv.org/abs/2402.08198 | https://arxiv.org/pdf/2402.08198v1 | 2402.08198 | 10.48550/arXiv.2402.08198 | 22 | 1 | false | null | AAAI Conference on Artificial Intelligence | 0.3404 |
6152b478148e883b6b67f2f7ce39b9dcf85e58a28cc4a0d08f29ba870cb5498e | [
"arxiv",
"semantic_scholar"
] | Do Membership Inference Attacks Work on Large Language Models? | Membership inference attacks (MIAs) attempt to predict whether a particular datapoint is a member of a target model's training data. Despite extensive research on traditional machine learning models, there has been limited work studying MIA on the pre-training data of large language models (LLMs). We perform a large-sc... | [
"Michael Duan",
"Anshuman Suri",
"Niloofar Mireshghallah",
"Sewon Min",
"Weijia Shi",
"Luke Zettlemoyer",
"Yulia Tsvetkov",
"Yejin Choi",
"David Evans",
"Hannaneh Hajishirzi"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-02-12T00:00:00 | https://arxiv.org/abs/2402.07841 | https://arxiv.org/pdf/2402.07841v2 | 2402.07841 | null | 217 | 34 | false | null | arXiv.org | 0.772 |
a4d05d6d7fa04c8f551b6e7015b8894144a07e894a3d4fa58469030ed72d5354 | [
"arxiv",
"semantic_scholar"
] | X-LoRA: Mixture of Low-Rank Adapter Experts, a Flexible Framework for Large Language Models with Applications in Protein Mechanics and Molecular Design | We report a mixture of expert strategy to create fine-tuned large language models using a deep layer-wise token-level approach based on low-rank adaptation (LoRA). Starting with a set of pre-trained LoRA adapters, our gating strategy uses the hidden states to dynamically mix adapted layers, allowing the resulting X-LoR... | [
"Eric L. Buehler",
"Markus J. Buehler"
] | [
"cond-mat.soft",
"cond-mat.dis-nn",
"cs.AI",
"cs.CL",
"cs.LG",
"q-bio.QM"
] | [
"Computer Science",
"Physics",
"Biology"
] | 2024-02-11T00:00:00 | https://arxiv.org/abs/2402.07148 | https://arxiv.org/pdf/2402.07148v2 | 2402.07148 | 10.48550/arXiv.2402.07148 | 62 | 1 | false | null | APL Machine Learning | 0.4498 |
8567ed94c811fb0acafc4bf47e5ad5a9ea8e1da988728f978eb076b99d1de874 | [
"arxiv",
"semantic_scholar"
] | Structure-Informed Protein Language Model | Protein language models are a powerful tool for learning protein representations through pre-training on vast protein sequence datasets. However, traditional protein language models lack explicit structural supervision, despite its relevance to protein function. To address this issue, we introduce the integration of re... | [
"Zuobai Zhang",
"Jiarui Lu",
"Vijil Chenthamarakshan",
"Aurélie Lozano",
"Payel Das",
"Jian Tang"
] | [
"q-bio.BM",
"cs.LG"
] | [
"Computer Science",
"Biology"
] | 2024-02-07T00:00:00 | https://arxiv.org/abs/2402.05856 | https://arxiv.org/pdf/2402.05856v1 | 2402.05856 | 10.48550/arXiv.2402.05856 | 15 | 1 | true | https://github.com/DeepGraphLearning/esm-s | arXiv.org | 0.301 |
1b5f3e7b743400689022dd8e7a65b34aee90f3aa1c54b82344006df5df4ef40e | [
"arxiv",
"semantic_scholar"
] | AlphaFold Meets Flow Matching for Generating Protein Ensembles | The biological functions of proteins often depend on dynamic structural ensembles. In this work, we develop a flow-based generative modeling approach for learning and sampling the conformational landscapes of proteins. We repurpose highly accurate single-state predictors such as AlphaFold and ESMFold and fine-tune them... | [
"Bowen Jing",
"Bonnie Berger",
"Tommi Jaakkola"
] | [
"q-bio.BM",
"cs.LG"
] | [
"Computer Science",
"Biology"
] | 2024-02-07T00:00:00 | https://arxiv.org/abs/2402.04845 | https://arxiv.org/pdf/2402.04845v2 | 2402.04845 | 10.48550/arXiv.2402.04845 | 248 | 29 | true | https://github.com/bjing2016/alphaflow | International Conference on Machine Learning | 0.7386 |
b81cd57a435f3ce758b52e1895bf8bf730a90b96d5d694c2442416da4e683c36 | [
"arxiv",
"semantic_scholar"
] | Learning immune receptor representations with protein language models | Protein language models (PLMs) learn contextual representations from protein sequences and are profoundly impacting various scientific disciplines spanning protein design, drug discovery, and structural predictions. One particular research area where PLMs have gained considerable attention is adaptive immune receptors,... | [
"Andreas Dounas",
"Tudor-Stefan Cotet",
"Alexander Yermanos"
] | [
"q-bio.QM"
] | [
"Biology"
] | 2024-02-06T00:00:00 | https://arxiv.org/abs/2402.03823 | https://arxiv.org/pdf/2402.03823v1 | 2402.03823 | null | 6 | 1 | false | null | null | 0.2113 |
fb085fe9398a841835cd9372771b9700e807793afcd55db9c85ad2ce34045fd2 | [
"arxiv",
"semantic_scholar"
] | Detecting Mode Collapse in Language Models via Narration | No two authors write alike. Personal flourishes invoked in written narratives, from lexicon to rhetorical devices, imply a particular author--what literary theorists label the implied or virtual author; distinct from the real author or narrator of a text. Early large language models trained on unfiltered training sets ... | [
"Sil Hamilton"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-02-06T00:00:00 | https://arxiv.org/abs/2402.04477 | https://arxiv.org/pdf/2402.04477v1 | 2402.04477 | 10.48550/arXiv.2402.04477 | 23 | 2 | false | null | https://aclanthology.org/2024.scalellm-1.5/ | 0.3451 |
fd4cfe0bd527b1bd43e1d6ea9570458bf2d506069cc816171da590de9bfcc101 | [
"arxiv",
"semantic_scholar"
] | idMotif: An Interactive Motif Identification in Protein Sequences | This article introduces idMotif, a visual analytics framework designed to aid domain experts in the identification of motifs within protein sequences. Motifs, short sequences of amino acids, are critical for understanding the distinct functions of proteins. Identifying these motifs is pivotal for predicting diseases or... | [
"Ji Hwan Park",
"Vikash Prasad",
"Sydney Newsom",
"Fares Najar",
"Rakhi Rajan"
] | [
"q-bio.QM",
"cs.GR",
"cs.HC",
"cs.LG"
] | [
"Computer Science",
"Medicine",
"Biology"
] | 2024-02-04T00:00:00 | https://arxiv.org/abs/2402.05953 | https://arxiv.org/pdf/2402.05953v1 | 2402.05953 | 10.1109/MCG.2023.3345742 | 1 | 0 | false | null | IEEE Computer Graphics and Applications | 0.0753 |
2f8b82da1e82d858fbc26124887f99cefef76b9f162500cdc450cc0476227f11 | [
"arxiv",
"semantic_scholar"
] | Enhancing the efficiency of protein language models with minimal wet-lab data through few-shot learning | Accurately modeling the protein fitness landscapes holds great importance for protein engineering. Recently, due to their capacity and representation ability, pre-trained protein language models have achieved state-of-the-art performance in predicting protein fitness without experimental data. However, their prediction... | [
"Ziyi Zhou",
"Liang Zhang",
"Yuanxi Yu",
"Mingchen Li",
"Liang Hong",
"Pan Tan"
] | [
"q-bio.BM"
] | [
"Biology"
] | 2024-02-03T00:00:00 | https://arxiv.org/abs/2402.02004 | https://arxiv.org/pdf/2402.02004v1 | 2402.02004 | null | 29 | 0 | false | null | null | 0.3693 |
3d9e936cd078517bd1fe6d4648569f3659b30448339c583e849328caced87184 | [
"arxiv",
"semantic_scholar"
] | ProtAgents: Protein discovery via large language model multi-agent collaborations combining physics and machine learning | Designing de novo proteins beyond those found in nature holds significant promise for advancements in both scientific and engineering applications. Current methodologies for protein design often rely on AI-based models, such as surrogate models that address end-to-end problems by linking protein structure to material p... | [
"A. Ghafarollahi",
"M. J. Buehler"
] | [
"cond-mat.soft",
"cs.AI",
"cs.CL",
"q-bio.BM"
] | [
"Physics",
"Computer Science",
"Biology",
"Medicine"
] | 2024-01-27T00:00:00 | https://arxiv.org/abs/2402.04268 | https://arxiv.org/pdf/2402.04268v1 | 2402.04268 | 10.48550/arXiv.2402.04268 | 95 | 3 | false | null | Digital Discovery | 0.4956 |
c918f07b1044ab6165a8297f19832449868e3fdceabfe75befb2e433ed4ed2ec | [
"arxiv",
"semantic_scholar"
] | Endowing Protein Language Models with Structural Knowledge | Understanding the relationships between protein sequence, structure and function is a long-standing biological challenge with manifold implications from drug design to our understanding of evolution. Recently, protein language models have emerged as the preferred method for this challenge, thanks to their ability to ha... | [
"Dexiong Chen",
"Philip Hartout",
"Paolo Pellizzoni",
"Carlos Oliver",
"Karsten Borgwardt"
] | [
"q-bio.QM",
"cs.LG",
"q-bio.BM"
] | [
"Computer Science",
"Biology"
] | 2024-01-26T00:00:00 | https://arxiv.org/abs/2401.14819 | https://arxiv.org/pdf/2401.14819v1 | 2401.14819 | 10.48550/arXiv.2401.14819 | 22 | 1 | true | https://github.com/BorgwardtLab/PST | arXiv.org | 0.3404 |
430fe979c85df95218bd9cc2f622d91b779bb0843c78d836fab1ec81156f253e | [
"arxiv",
"semantic_scholar"
] | Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap | Large language models (LLMs) have not only revolutionized natural language processing but also extended their prowess to various domains, marking a significant stride towards artificial general intelligence. The interplay between LLMs and evolutionary algorithms (EAs), despite differing in objectives and methodologies,... | [
"Xingyu Wu",
"Sheng-hao Wu",
"Jibin Wu",
"Liang Feng",
"Kay Chen Tan"
] | [
"cs.NE",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2024-01-18T00:00:00 | https://arxiv.org/abs/2401.10034 | https://arxiv.org/pdf/2401.10034v3 | 2401.10034 | 10.1109/TEVC.2024.3506731 | 181 | 4 | true | https://github.com/wuxingyu-ai/LLM4EC | IEEE Transactions on Evolutionary Computation | 0.565 |
41713d942bb1746a0ee489db986fbd387a9cc8313e22da375315c07efebfd623 | [
"arxiv",
"semantic_scholar"
] | Part-of-Speech Tagger for Bodo Language using Deep Learning approach | Language Processing systems such as Part-of-speech tagging, Named entity recognition, Machine translation, Speech recognition, and Language modeling (LM) are well-studied in high-resource languages. Nevertheless, research on these systems for several low-resource languages, including Bodo, Mizo, Nagamese, and others, i... | [
"Dhrubajyoti Pathak",
"Sanjib Narzary",
"Sukumar Nandi",
"Bidisha Som"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2024-01-06T00:00:00 | https://arxiv.org/abs/2401.03175 | https://arxiv.org/pdf/2401.03175v1 | 2401.03175 | 10.1017/nlp.2024.15 | 7 | 0 | false | null | Natural Language Processing | 0.2258 |
2fa521d10b2118188e460da13c2744ad40d4f1140016c5b8f2c3fe00778a9c71 | [
"arxiv",
"semantic_scholar"
] | ACP-ESM: A novel framework for classification of anticancer peptides using protein-oriented transformer approach | Anticancer peptides (ACPs) are a class of molecules that have gained significant attention in the field of cancer research and therapy. ACPs are short chains of amino acids, the building blocks of proteins, and they possess the ability to selectively target and kill cancer cells. One of the key advantages of ACPs is th... | [
"Zeynep Hilal Kilimci",
"Mustafa Yalcin"
] | [
"q-bio.BM",
"cs.AI",
"cs.CE",
"cs.LG"
] | [
"Medicine",
"Computer Science",
"Biology"
] | 2024-01-04T00:00:00 | https://arxiv.org/abs/2401.02124 | https://arxiv.org/pdf/2401.02124v1 | 2401.02124 | 10.48550/arXiv.2401.02124 | 14 | 0 | false | null | null | 0.294 |
157542eac9f9fb89d45719a0a43aec218affed145b09d578667be3383fcba9ef | [
"arxiv",
"semantic_scholar"
] | Identification of Knowledge Neurons in Protein Language Models | Neural language models have become powerful tools for learning complex representations of entities in natural language processing tasks. However, their interpretability remains a significant challenge, particularly in domains like computational biology where trust in model predictions is crucial. In this work, we aim t... | [
"Divya Nori",
"Shivali Singireddy",
"Marina Ten Have"
] | [
"cs.LG",
"cs.AI",
"cs.CL",
"q-bio.BM"
] | [
"Computer Science",
"Biology"
] | 2023-12-17T00:00:00 | https://arxiv.org/abs/2312.10770 | https://arxiv.org/pdf/2312.10770v1 | 2312.10770 | 10.48550/arXiv.2312.10770 | 4 | 0 | false | null | arXiv.org | 0.1747 |
fad3b06eab1c317aa9fda2813083959535c503618961a660e23072540982672d | [
"arxiv",
"semantic_scholar"
] | Demystifying Instruction Mixing for Fine-tuning Large Language Models | Instruction tuning significantly enhances the performance of large language models (LLMs) across various tasks. However, the procedure to optimizing the mixing of instruction datasets for LLM fine-tuning is still poorly understood. This study categorizes instructions into three primary types: NLP downstream tasks, codi... | [
"Renxi Wang",
"Haonan Li",
"Minghao Wu",
"Yuxia Wang",
"Xudong Han",
"Chiyu Zhang",
"Timothy Baldwin"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2023-12-17T00:00:00 | https://arxiv.org/abs/2312.10793 | https://arxiv.org/pdf/2312.10793v3 | 2312.10793 | 10.18653/v1/2024.acl-srw.15 | 8 | 0 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.2386 |
9438d738250c9b7e92a49cf30c6731c4c24d0dc520f3a0d6c8957ac9a4b41d70 | [
"arxiv",
"semantic_scholar"
] | Beneath the Surface: Unveiling Harmful Memes with Multimodal Reasoning Distilled from Large Language Models | The age of social media is rife with memes. Understanding and detecting harmful memes pose a significant challenge due to their implicit meaning that is not explicitly conveyed through the surface text and image. However, existing harmful meme detection approaches only recognize superficial harm-indicative signals in a... | [
"Hongzhan Lin",
"Ziyang Luo",
"Jing Ma",
"Long Chen"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2023-12-09T00:00:00 | https://arxiv.org/abs/2312.05434 | https://arxiv.org/pdf/2312.05434v1 | 2312.05434 | 10.18653/v1/2023.findings-emnlp.611 | 27 | 2 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.3618 |
830d7d6cce586c2b9f8f0a9cf11b2d2e937491630cdad176be024e936edc6f37 | [
"arxiv",
"semantic_scholar"
] | Efficiently Predicting Protein Stability Changes Upon Single-point Mutation with Large Language Models | Predicting protein stability changes induced by single-point mutations has been a persistent challenge over the years, attracting immense interest from numerous researchers. The ability to precisely predict protein thermostability is pivotal for various subfields and applications in biochemistry, including drug develop... | [
"Yijie Zhang",
"Zhangyang Gao",
"Cheng Tan",
"Stan Z. Li"
] | [
"q-bio.BM",
"cs.AI"
] | [
"Biology",
"Computer Science"
] | 2023-12-07T00:00:00 | https://arxiv.org/abs/2312.04019 | https://arxiv.org/pdf/2312.04019v1 | 2312.04019 | 10.48550/arXiv.2312.04019 | 3 | 0 | false | null | arXiv.org | 0.1505 |
4f084ea725e9aaa737730044e57b254527a6592d2a675a7049cd6043006f7198 | [
"arxiv",
"semantic_scholar"
] | Using a Large Language Model to generate a Design Structure Matrix | The Design Structure Matrix (DSM) is an established method used in dependency modelling, especially in the design of complex engineering systems. The generation of DSM is traditionally carried out through manual means and can involve interviewing experts to elicit critical system elements and the relationships between ... | [
"Edwin C. Y. Koh"
] | [
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2023-12-07T00:00:00 | https://arxiv.org/abs/2312.04134 | https://arxiv.org/pdf/2312.04134v1 | 2312.04134 | 10.1016/j.nlp.2024.100103 | 5 | 0 | false | null | Natural Language Processing Journal | 0.1945 |
2792a97a581ecad2d219f85cfb3cc2d13788ec28cf60f8b9723d84f3937bd38a | [
"arxiv",
"semantic_scholar"
] | Protein Language Model-Powered 3D Ligand Binding Site Prediction from Protein Sequence | Prediction of ligand binding sites of proteins is a fundamental and important task for understanding the function of proteins and screening potential drugs. Most existing methods require experimentally determined protein holo-structures as input. However, such structures can be unavailable on novel or less-studied prot... | [
"Shuo Zhang",
"Lei Xie"
] | [
"q-bio.QM",
"cs.CL",
"cs.LG"
] | [
"Biology",
"Computer Science"
] | 2023-12-05T00:00:00 | https://arxiv.org/abs/2312.03016 | https://arxiv.org/pdf/2312.03016v1 | 2312.03016 | 10.48550/arXiv.2312.03016 | 10 | 1 | false | null | arXiv.org | 0.2603 |
93ba3aa005e82c440388e61de11cadb1e28c33bad7c9da202fe42cb094499353 | [
"arxiv",
"semantic_scholar"
] | ESM-NBR: fast and accurate nucleic acid-binding residue prediction via protein language model feature representation and multi-task learning | Protein-nucleic acid interactions play a very important role in a variety of biological activities. Accurate identification of nucleic acid-binding residues is a critical step in understanding the interaction mechanisms. Although many computationally based methods have been developed to predict nucleic acid-binding res... | [
"Wenwu Zeng",
"Dafeng Lv",
"Wenjuan Liu",
"Shaoliang Peng"
] | [
"q-bio.QM",
"cs.LG"
] | [
"Biology",
"Computer Science"
] | 2023-12-01T00:00:00 | https://arxiv.org/abs/2312.00842 | https://arxiv.org/pdf/2312.00842v1 | 2312.00842 | 10.1109/BIBM58861.2023.10385509 | 8 | 0 | true | https://github.com/wwzll123/ESM-NBR | IEEE International Conference on Bioinformatics and Biomedicine | 0.2386 |
b41de1ba79d23211754acef5ceee8a3cc8ba11133ac1e9a857834fdafda2d3eb | [
"arxiv",
"semantic_scholar"
] | A perspective on protein structure prediction using quantum computers | Despite the recent advancements by deep learning methods such as AlphaFold2, \textit{in silico} protein structure prediction remains a challenging problem in biomedical research. With the rapid evolution of quantum computing, it is natural to ask whether quantum computers can offer some meaningful benefits for approach... | [
"Hakan Doga",
"Bryan Raubenolt",
"Fabio Cumbo",
"Jayadev Joshi",
"Frank P. DiFilippo",
"Jun Qin",
"Daniel Blankenberg",
"Omar Shehab"
] | [
"quant-ph"
] | [
"Physics",
"Medicine"
] | 2023-12-01T00:00:00 | https://arxiv.org/abs/2312.00875 | https://arxiv.org/pdf/2312.00875v1 | 2312.00875 | 10.1021/acs.jctc.4c00067 | 50 | 1 | false | null | Journal of Chemical Theory and Computation | 0.4269 |
ecc7d6bb5abf282df6a48a895466f03eacf25aaea8fe124a09eb8067e9ad9af1 | [
"arxiv",
"semantic_scholar"
] | Acoustic Prompt Tuning: Empowering Large Language Models with Audition Capabilities | The auditory system plays a substantial role in shaping the overall human perceptual experience. While prevailing large language models (LLMs) and visual language models (VLMs) have shown their promise in solving a wide variety of language and vision understanding tasks, only a few of them can be generalised to the aud... | [
"Jinhua Liang",
"Xubo Liu",
"Wenwu Wang",
"Mark D. Plumbley",
"Huy Phan",
"Emmanouil Benetos"
] | [
"eess.AS"
] | [
"Engineering"
] | 2023-11-30T00:00:00 | https://arxiv.org/abs/2312.00249 | https://arxiv.org/pdf/2312.00249v2 | 2312.00249 | 10.1109/TASLPRO.2025.3533375 | 24 | 1 | true | https://github.com/JinhuaLiang/APT | IEEE Transactions on Audio, Speech, and Language Processing | 0.3495 |
f44525bd159e0fb1c03ffe0c5b1d2d856e535635fb152a020c8a95121b5e301f | [
"arxiv",
"semantic_scholar"
] | When a Language Question Is at Stake. A Revisited Approach to Label Sensitive Content | Many under-resourced languages require high-quality datasets for specific tasks such as offensive language detection, disinformation, or misinformation identification. However, the intricacies of the content may have a detrimental effect on the annotators. The article aims to revisit an approach of pseudo-labeling sens... | [
"Stetsenko Daria"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2023-11-17T00:00:00 | https://arxiv.org/abs/2311.10514 | https://arxiv.org/pdf/2311.10514v1 | 2311.10514 | 10.48550/arXiv.2311.10514 | 2 | 0 | false | null | arXiv.org | 0.1193 |
cd67b4d29cbff9423c4daa1e0bd25558dcb9305aa7c799bd807e62a6ac03bc79 | [
"arxiv",
"semantic_scholar"
] | Efficiently Adapting Pretrained Language Models To New Languages | Recent large language models (LLM) exhibit sub-optimal performance on low-resource languages, as the training data of these models is usually dominated by English and other high-resource languages. Furthermore, it is challenging to train models for low-resource languages, especially from scratch, due to a lack of high ... | [
"Zoltan Csaki",
"Pian Pawakapan",
"Urmish Thakker",
"Qiantong Xu"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2023-11-09T00:00:00 | https://arxiv.org/abs/2311.05741 | https://arxiv.org/pdf/2311.05741v2 | 2311.05741 | 10.48550/arXiv.2311.05741 | 33 | 4 | true | null | arXiv.org | 0.3829 |
9e8ccae539771af72541e502e79594757be3df8ab5317589d61ca0c7f813d676 | [
"arxiv",
"semantic_scholar"
] | Impact of the Ce $4f$ states in the electronic structure of the intermediate-valence superconductor CeIr$_3$ | The electronic structure of the $f$-based superconductor $\mathrm{CeIr_3}$ was studied by photoelectron spectroscopy. The energy distribution of the $\mathrm{Ce}~4f$ states were revealed by the $\mathrm{Ce}~3d-4f$ resonant photoelectron spectroscopy. The $\mathrm{Ce}~4f$ states were mostly distributed in the vicinity o... | [
"Shin-ichi Fujimori",
"Ikuto Kawasaki",
"Yukiharu Takeda",
"Hiroshi Yamagami",
"Norimasa Sasabe",
"Yoshiki J. Sato",
"Ai Nakamura",
"Yusei Shimizu",
"Arvind Maurya",
"Yoshiya Homma",
"Dexin Li",
"Fuminori Honda",
"Dai Aoki"
] | [
"cond-mat.str-el",
"cond-mat.supr-con"
] | [
"Physics"
] | 2023-11-07T00:00:00 | https://arxiv.org/abs/2311.03640 | https://arxiv.org/pdf/2311.03640v1 | 2311.03640 | 10.1088/2516-1075/ad0a3d | 0 | 0 | false | null | Electronic Structure | 0 |
202b5db5492de11698d8518a7534bc87276f14ec3f24b48215395d1c9edb48ce | [
"arxiv",
"semantic_scholar"
] | Can Language Models Be Tricked by Language Illusions? Easier with Syntax, Harder with Semantics | Language models (LMs) have been argued to overlap substantially with human beings in grammaticality judgment tasks. But when humans systematically make errors in language processing, should we expect LMs to behave like cognitive models of language and mimic human behavior? We answer this question by investigating LMs' ... | [
"Yuhan Zhang",
"Edward Gibson",
"Forrest Davis"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2023-11-02T00:00:00 | https://arxiv.org/abs/2311.01386 | https://arxiv.org/pdf/2311.01386v2 | 2311.01386 | 10.18653/v1/2023.conll-1.1 | 8 | 1 | false | null | Conference on Computational Natural Language Learning | 0.2386 |
e2fbc9168d24e9c011e6ed8f23d41ac3dc7fe947b022b22ba95e7d317447bfa8 | [
"arxiv",
"semantic_scholar"
] | Exploring Post-Training Quantization of Protein Language Models | Recent advancements in unsupervised protein language models (ProteinLMs), like ESM-1b and ESM-2, have shown promise in different protein prediction tasks. However, these models face challenges due to their high computational demands, significant memory needs, and latency, restricting their usage on devices with limited... | [
"Shuang Peng",
"Fei Yang",
"Ning Sun",
"Sheng Chen",
"Yanfeng Jiang",
"Aimin Pan"
] | [
"cs.LG",
"cs.AI",
"q-bio.QM"
] | [
"Computer Science",
"Biology"
] | 2023-10-30T00:00:00 | https://arxiv.org/abs/2310.19624 | https://arxiv.org/pdf/2310.19624v1 | 2310.19624 | 10.1109/BIBM58861.2023.10385775 | 0 | 0 | false | null | IEEE International Conference on Bioinformatics and Biomedicine | 0 |
634c2cf0178fbc4edabe9241f2b824bf01875a929aeb821b964e6509992cfaf3 | [
"arxiv",
"semantic_scholar"
] | GPCR-BERT: Interpreting Sequential Design of G Protein Coupled Receptors Using Protein Language Models | With the rise of Transformers and Large Language Models (LLMs) in Chemistry and Biology, new avenues for the design and understanding of therapeutics have opened up to the scientific community. Protein sequences can be modeled as language and can take advantage of recent advances in LLMs, specifically with the abundanc... | [
"Seongwon Kim",
"Parisa Mollaei",
"Akshay Antony",
"Rishikesh Magar",
"Amir Barati Farimani"
] | [
"cs.LG",
"q-bio.BM"
] | [
"Computer Science",
"Biology",
"Medicine"
] | 2023-10-30T00:00:00 | https://arxiv.org/abs/2310.19915 | https://arxiv.org/pdf/2310.19915v1 | 2310.19915 | 10.1021/acs.jcim.3c01706 | 17 | 0 | false | null | Journal of Chemical Information and Modeling | 0.3138 |
06df6b82b7242860ccc174055d0dc1d0bd831a9a619d6d207ba5a3453961ea83 | [
"arxiv",
"semantic_scholar"
] | Large Language Models and Multimodal Retrieval for Visual Word Sense Disambiguation | Visual Word Sense Disambiguation (VWSD) is a novel challenging task with the goal of retrieving an image among a set of candidates, which better represents the meaning of an ambiguous word within a given context. In this paper, we make a substantial step towards unveiling this interesting task by applying a varying set... | [
"Anastasia Kritharoula",
"Maria Lymperaiou",
"Giorgos Stamou"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2023-10-21T00:00:00 | https://arxiv.org/abs/2310.14025 | https://arxiv.org/pdf/2310.14025v1 | 2310.14025 | 10.18653/v1/2023.emnlp-main.807 | 10 | 0 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.2603 |
c3b6fa685318d64bd0e2bc2f7af01c5b37567e5d261138e9c1c9961420714cfb | [
"arxiv",
"semantic_scholar"
] | ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a protein language diffusion model | Through evolution, nature has presented a set of remarkable protein materials, including elastins, silks, keratins and collagens with superior mechanical performances that play crucial roles in mechanobiology. However, going beyond natural designs to discover proteins that meet specified mechanical properties remains c... | [
"Bo Ni",
"David L. Kaplan",
"Markus J. Buehler"
] | [
"cond-mat.mtrl-sci",
"cond-mat.mes-hall",
"cs.CL",
"cs.LG",
"q-bio.BM"
] | [
"Computer Science",
"Physics",
"Biology",
"Medicine"
] | 2023-10-16T00:00:00 | https://arxiv.org/abs/2310.10605 | https://arxiv.org/pdf/2310.10605v3 | 2310.10605 | 10.48550/arXiv.2310.10605 | 5 | 0 | false | null | arXiv.org | 0.1945 |
89093eeb1c7ae6ee601b9244a24075254cf96f8a5d3a70ccfdc737cd8cd08467 | [
"arxiv",
"semantic_scholar"
] | Joint Music and Language Attention Models for Zero-shot Music Tagging | Music tagging is a task to predict the tags of music recordings. However, previous music tagging research primarily focuses on close-set music tagging tasks which can not be generalized to new tags. In this work, we propose a zero-shot music tagging system modeled by a joint music and language attention (JMLA) model to... | [
"Xingjian Du",
"Zhesong Yu",
"Jiaju Lin",
"Bilei Zhu",
"Qiuqiang Kong"
] | [
"cs.SD",
"cs.CL",
"eess.AS"
] | [
"Computer Science",
"Engineering"
] | 2023-10-16T00:00:00 | https://arxiv.org/abs/2310.10159 | https://arxiv.org/pdf/2310.10159v1 | 2310.10159 | 10.1109/ICASSP48485.2024.10447760 | 15 | 2 | false | null | IEEE International Conference on Acoustics, Speech, and Signal Processing | 0.301 |
dcab335dc09cd6f12d3c1fd70d5d10ae35fe37845b8b858c30762f26f961bb00 | [
"arxiv",
"semantic_scholar"
] | Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction | Protein structure-based property prediction has emerged as a promising approach for various biological tasks, such as protein function prediction and sub-cellular location estimation. The existing methods highly rely on experimental protein structure data and fail in scenarios where these data are unavailable. Predicte... | [
"Yufei Huang",
"Siyuan Li",
"Jin Su",
"Lirong Wu",
"Odin Zhang",
"Haitao Lin",
"Jingqi Qi",
"Zihan Liu",
"Zhangyang Gao",
"Yuyang Liu",
"Jiangbin Zheng",
"Stan. ZQ. Li"
] | [
"cs.LG",
"cs.AI",
"q-bio.QM"
] | [
"Computer Science",
"Biology"
] | 2023-10-14T00:00:00 | https://arxiv.org/abs/2310.11466 | https://arxiv.org/pdf/2310.11466v2 | 2310.11466 | 10.48550/arXiv.2310.11466 | 18 | 0 | false | null | AAAI Conference on Artificial Intelligence | 0.3197 |
361b7e3af0b692812d098f5ae248a268fe6c998a9e7b05bfdea77af1c241e0aa | [
"arxiv",
"semantic_scholar"
] | Humans and language models diverge when predicting repeating text | Language models that are trained on the next-word prediction task have been shown to accurately model human behavior in word prediction and reading speed. In contrast with these findings, we present a scenario in which the performance of humans and LMs diverges. We collected a dataset of human next-word predictions for... | [
"Aditya R. Vaidya",
"Javier Turek",
"Alexander G. Huth"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2023-10-10T00:00:00 | https://arxiv.org/abs/2310.06408 | https://arxiv.org/pdf/2310.06408v2 | 2310.06408 | 10.48550/arXiv.2310.06408 | 15 | 1 | true | https://github.com/HuthLab/lm-repeating-text | Conference on Computational Natural Language Learning | 0.301 |
f31bb63467688df60672e649c8fb9063df8069b9bc9c5f6d833b2550b5b47fba | [
"arxiv",
"semantic_scholar"
] | Growing ecosystem of deep learning methods for modeling protein$\unicode{x2013}$protein interactions | Numerous cellular functions rely on protein$\unicode{x2013}$protein interactions. Efforts to comprehensively characterize them remain challenged however by the diversity of molecular recognition mechanisms employed within the proteome. Deep learning has emerged as a promising approach for tackling this problem by explo... | [
"Julia R. Rogers",
"Gergő Nikolényi",
"Mohammed AlQuraishi"
] | [
"q-bio.BM",
"cs.LG"
] | [
"Biology",
"Computer Science",
"Medicine"
] | 2023-10-10T00:00:00 | https://arxiv.org/abs/2310.06725 | https://arxiv.org/pdf/2310.06725v2 | 2310.06725 | 10.48550/arXiv.2310.06725 | 9 | 0 | false | null | null | 0.25 |
11968fb1f30519423fc8cd131f2541da67034c72ffcbd30765eaa38747acda38 | [
"arxiv",
"semantic_scholar"
] | VQPL: Vector Quantized Protein Language | Is there a foreign language describing protein sequences and structures simultaneously? Protein structures, represented by continuous 3D points, have long posed a challenge due to the contrasting modeling paradigms of discrete sequences. To represent protein sequence-structure as discrete symbols, we propose a VQProtei... | [
"Zhangyang Gao",
"Cheng Tan",
"Stan Z. Li"
] | [
"cs.CE"
] | [
"Computer Science"
] | 2023-10-08T00:00:00 | https://arxiv.org/abs/2310.04985 | https://arxiv.org/pdf/2310.04985v1 | 2310.04985 | 10.48550/arXiv.2310.04985 | 7 | 0 | false | null | arXiv.org | 0.2258 |
6c4f17468cc3ae2785cd6725e7a910eaa7007408737bdc8613171652308449a8 | [
"arxiv",
"semantic_scholar"
] | PGraphDTA: Improving Drug Target Interaction Prediction using Protein Language Models and Contact Maps | Developing and discovering new drugs is a complex and resource-intensive endeavor that often involves substantial costs, time investment, and safety concerns. A key aspect of drug discovery involves identifying novel drug-target (DT) interactions. Existing computational methods for predicting DT interactions have prima... | [
"Rakesh Bal",
"Yijia Xiao",
"Wei Wang"
] | [
"cs.LG",
"q-bio.QM"
] | [
"Computer Science",
"Biology"
] | 2023-10-06T00:00:00 | https://arxiv.org/abs/2310.04017 | https://arxiv.org/pdf/2310.04017v3 | 2310.04017 | 10.48550/arXiv.2310.04017 | 6 | 0 | true | https://github.com/Yijia-Xiao/PgraphDTA/ | arXiv.org | 0.2113 |
9f71c553ac494a20e1db34707694b9703cf41a23d0caddf2dbf6621c16f4c76c | [
"arxiv",
"semantic_scholar"
] | InstructProtein: Aligning Human and Protein Language via Knowledge Instruction | Large Language Models (LLMs) have revolutionized the field of natural language processing, but they fall short in comprehending biological sequences such as proteins. To address this challenge, we propose InstructProtein, an innovative LLM that possesses bidirectional generation capabilities in both human and protein l... | [
"Zeyuan Wang",
"Qiang Zhang",
"Keyan Ding",
"Ming Qin",
"Xiang Zhuang",
"Xiaotong Li",
"Huajun Chen"
] | [
"q-bio.BM",
"cs.CL"
] | [
"Computer Science",
"Biology"
] | 2023-10-05T00:00:00 | https://arxiv.org/abs/2310.03269 | https://arxiv.org/pdf/2310.03269v1 | 2310.03269 | 10.48550/arXiv.2310.03269 | 40 | 1 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.4032 |
cfa49c929a45cf3d611e30bbaa3596c403ce7fe03f7ea34732e0a8cb8e9e2c3b | [
"arxiv",
"semantic_scholar"
] | CrysFormer: Protein Structure Prediction via 3d Patterson Maps and Partial Structure Attention | Determining the structure of a protein has been a decades-long open question. A protein's three-dimensional structure often poses nontrivial computation costs, when classical simulation algorithms are utilized. Advances in the transformer neural network architecture -- such as AlphaFold2 -- achieve significant improvem... | [
"Chen Dun",
"Qiutai Pan",
"Shikai Jin",
"Ria Stevens",
"Mitchell D. Miller",
"George N. Phillips,",
"Anastasios Kyrillidis"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2023-10-05T00:00:00 | https://arxiv.org/abs/2310.03899 | https://arxiv.org/pdf/2310.03899v1 | 2310.03899 | 10.48550/arXiv.2310.03899 | 2 | 0 | false | null | arXiv.org | 0.1193 |
3ae1a41de90b8fd376c39a07f3979c4f1402250234232ce12ad8a5dab07ad991 | [
"arxiv",
"semantic_scholar"
] | All Languages Matter: On the Multilingual Safety of Large Language Models | Safety lies at the core of developing and deploying large language models (LLMs). However, previous safety benchmarks only concern the safety in one language, e.g. the majority language in the pretraining data such as English. In this work, we build the first multilingual safety benchmark for LLMs, XSafety, in response... | [
"Wenxuan Wang",
"Zhaopeng Tu",
"Chang Chen",
"Youliang Yuan",
"Jen-tse Huang",
"Wenxiang Jiao",
"Michael R. Lyu"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2023-10-02T00:00:00 | https://arxiv.org/abs/2310.00905 | https://arxiv.org/pdf/2310.00905v2 | 2310.00905 | 10.48550/arXiv.2310.00905 | 50 | 5 | true | https://github.com/Jarviswang94/Multilingual_safety_benchmark | arXiv.org | 0.4269 |
65372dce4caf433ef4defe378813c06c16895b8a33e6bb0ad84a3d05a759c0b7 | [
"arxiv",
"semantic_scholar"
] | PB-LLM: Partially Binarized Large Language Models | This paper explores network binarization, a radical form of quantization, compressing model weights to a single bit, specifically for Large Language Models (LLMs) compression. Due to previous binarization methods collapsing LLMs, we propose a novel approach, Partially-Binarized LLM (PB-LLM), which can achieve extreme l... | [
"Yuzhang Shang",
"Zhihang Yuan",
"Qiang Wu",
"Zhen Dong"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2023-09-29T00:00:00 | https://arxiv.org/abs/2310.00034 | https://arxiv.org/pdf/2310.00034v2 | 2310.00034 | 10.48550/arXiv.2310.00034 | 95 | 15 | true | https://github.com/hahnyuan/BinaryLLM | International Conference on Learning Representations | 0.6021 |
7230ecfbb022cebc7c7f39de0dc235be982dbc84969c15e212e0c203da79972c | [
"arxiv",
"semantic_scholar"
] | pLMFPPred: a novel approach for accurate prediction of functional peptides integrating embedding from pre-trained protein language model and imbalanced learning | Functional peptides have the potential to treat a variety of diseases. Their good therapeutic efficacy and low toxicity make them ideal therapeutic agents. Artificial intelligence-based computational strategies can help quickly identify new functional peptides from collections of protein sequences and discover their di... | [
"Zebin Ma",
"Yonglin Zou",
"Xiaobin Huang",
"Wenjin Yan",
"Hao Xu",
"Jiexin Yang",
"Ying Zhang",
"Jinqi Huang"
] | [
"q-bio.QM",
"cs.LG"
] | [
"Computer Science",
"Biology"
] | 2023-09-25T00:00:00 | https://arxiv.org/abs/2309.14404 | https://arxiv.org/pdf/2309.14404v1 | 2309.14404 | 10.48550/arXiv.2309.14404 | 4 | 0 | false | null | arXiv.org | 0.1747 |
d2dd2d2d7a18e63289156f8dcf91630610e899794fd94a3681e763db652b77af | [
"arxiv",
"semantic_scholar"
] | Speaker attribution in German parliamentary debates with QLoRA-adapted large language models | The growing body of political texts opens up new opportunities for rich insights into political dynamics and ideologies but also increases the workload for manual analysis. Automated speaker attribution, which detects who said what to whom in a speech event and is closely related to semantic role labeling, is an import... | [
"Tobias Bornheim",
"Niklas Grieger",
"Patrick Gustav Blaneck",
"Stephan Bialonski"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2023-09-18T00:00:00 | https://arxiv.org/abs/2309.09902 | https://arxiv.org/pdf/2309.09902v2 | 2309.09902 | 10.21248/jlcl.37.2024.244 | 2 | 0 | false | null | Journal for Language Technology and Computational Linguistics | 0.1193 |
1228aed58d0055d19b8c959ff70dd01706e62c39d913f12f1e0e3648a1b174a6 | [
"arxiv",
"semantic_scholar"
] | Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models | We introduce Jais and Jais-chat, new state-of-the-art Arabic-centric foundation and instruction-tuned open generative large language models (LLMs). The models are based on the GPT-3 decoder-only architecture and are pretrained on a mixture of Arabic and English texts, including source code in various programming langua... | [
"Neha Sengupta",
"Sunil Kumar Sahu",
"Bokang Jia",
"Satheesh Katipomu",
"Haonan Li",
"Fajri Koto",
"William Marshall",
"Gurpreet Gosal",
"Cynthia Liu",
"Zhiming Chen",
"Osama Mohammed Afzal",
"Samta Kamboj",
"Onkar Pandit",
"Rahul Pal",
"Lalit Pradhan",
"Zain Muhammad Mujahid",
"Mass... | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2023-08-30T00:00:00 | https://arxiv.org/abs/2308.16149 | https://arxiv.org/pdf/2308.16149v2 | 2308.16149 | 10.48550/arXiv.2308.16149 | 81 | 8 | false | null | arXiv.org | 0.4785 |
b4998bcc11ee75a6f15470d1055606546eff53b29cf64efe7465db3b36b9531a | [
"arxiv",
"semantic_scholar"
] | Atom-by-atom protein generation and beyond with language models | Protein language models learn powerful representations directly from sequences of amino acids. However, they are constrained to generate proteins with only the set of amino acids represented in their vocabulary. In contrast, chemical language models learn atom-level representations of smaller molecules that include eve... | [
"Daniel Flam-Shepherd",
"Kevin Zhu",
"Alán Aspuru-Guzik"
] | [
"q-bio.BM",
"cs.LG"
] | [
"Biology",
"Computer Science"
] | 2023-08-16T00:00:00 | https://arxiv.org/abs/2308.09482 | https://arxiv.org/pdf/2308.09482v1 | 2308.09482 | 10.48550/arXiv.2308.09482 | 3 | 0 | false | null | arXiv.org | 0.1505 |
165d61fc97b194ebbd7908388675d1dc3d0baa5a9a690e0a7c6d1b30fdd73a13 | [
"arxiv",
"semantic_scholar"
] | PEvoLM: Protein Sequence Evolutionary Information Language Model | With the exponential increase of the protein sequence databases over time, multiple-sequence alignment (MSA) methods, like PSI-BLAST, perform exhaustive and time-consuming database search to retrieve evolutionary information. The resulting position-specific scoring matrices (PSSMs) of such search engines represent a cr... | [
"Issar Arab"
] | [
"q-bio.QM",
"cs.AI",
"cs.LG"
] | [
"Computer Science",
"Biology"
] | 2023-08-16T00:00:00 | https://arxiv.org/abs/2308.08578 | https://arxiv.org/pdf/2308.08578v1 | 2308.08578 | 10.1109/CIBCB56990.2023.10264890 | 2 | 0 | true | https://github.com/issararab/PEvoLM | IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology | 0.1193 |
79ee2d23699659e8f8dfd6574dadff8595d3f1d64fc3fbe285910f5670d64158 | [
"arxiv",
"semantic_scholar"
] | Pairing interacting protein sequences using masked language modeling | Predicting which proteins interact together from amino-acid sequences is an important task. We develop a method to pair interacting protein sequences which leverages the power of protein language models trained on multiple sequence alignments, such as MSA Transformer and the EvoFormer module of AlphaFold. We formulate ... | [
"Umberto Lupo",
"Damiano Sgarbossa",
"Anne-Florence Bitbol"
] | [
"q-bio.BM",
"cs.LG"
] | [
"Biology",
"Computer Science",
"Medicine"
] | 2023-08-14T00:00:00 | https://arxiv.org/abs/2308.07136 | https://arxiv.org/pdf/2308.07136v1 | 2308.07136 | 10.1073/pnas.2311887121 | 25 | 0 | false | null | bioRxiv | 0.3537 |
21f4235e78a1427494d2450af10a9541f7dbcf443ccf0d7c985287a6da7a244b | [
"arxiv",
"semantic_scholar"
] | FFF: Fragments-Guided Flexible Fitting for Building Complete Protein Structures | Cryo-electron microscopy (cryo-EM) is a technique for reconstructing the 3-dimensional (3D) structure of biomolecules (especially large protein complexes and molecular assemblies). As the resolution increases to the near-atomic scale, building protein structures de novo from cryo-EM maps becomes possible. Recently, rec... | [
"Weijie Chen",
"Xinyan Wang",
"Yuhang Wang"
] | [
"cs.CV",
"cs.AI",
"q-bio.BM",
"q-bio.QM"
] | [
"Computer Science",
"Biology"
] | 2023-08-07T00:00:00 | https://arxiv.org/abs/2308.03654 | https://arxiv.org/pdf/2308.03654v1 | 2308.03654 | 10.48550/arXiv.2308.03654 | 0 | 0 | false | null | arXiv.org | 0 |
0a642f9f6efbfc66378e8980e6f5b9942da1ba7d30574abde9f6f6d54e154e66 | [
"arxiv",
"semantic_scholar"
] | Turkish Native Language Identification V2 | This paper presents the first application of Native Language Identification (NLI) for the Turkish language. NLI is the task of automatically identifying an individual's native language (L1) based on their writing or speech in a non-native language (L2). While most NLI research has focused on L2 English, our study exten... | [
"Ahmet Yavuz Uluslu",
"Gerold Schneider"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2023-07-27T00:00:00 | https://arxiv.org/abs/2307.14850 | https://arxiv.org/pdf/2307.14850v6 | 2307.14850 | 10.48550/arXiv.2307.14850 | 1 | 0 | false | null | International Conference on Natural Language and Speech Processing | 0.0753 |
4688455352a2350cb465f83bacb075b7c6a9c5b5c763f6fb34dbcf797ee6fbc5 | [
"arxiv",
"semantic_scholar"
] | Artificial intelligence-aided protein engineering: from topological data analysis to deep protein language models | Protein engineering is an emerging field in biotechnology that has the potential to revolutionize various areas, such as antibody design, drug discovery, food security, ecology, and more. However, the mutational space involved is too vast to be handled through experimental means alone. Leveraging accumulative protein d... | [
"Yuchi Qiu",
"Guo-Wei Wei"
] | [
"q-bio.BM"
] | [
"Biology",
"Medicine",
"Computer Science"
] | 2023-07-27T00:00:00 | https://arxiv.org/abs/2307.14587 | https://arxiv.org/pdf/2307.14587v1 | 2307.14587 | 10.1093/bib/bbad289 | 58 | 0 | false | null | null | 0.4427 |
94ee3581db609718775287ea45a7d4826c8c10bb02973a8493afe0f5c420d1b8 | [
"arxiv",
"semantic_scholar"
] | Predicting Relative Populations of Protein Conformations without a Physics Engine Using AlphaFold2 | This paper presents a novel approach for predicting the relative populations of protein conformations using AlphaFold 2, an AI-powered method that has revolutionized biology by enabling the accurate prediction of protein structures. While AlphaFold 2 has shown exceptional accuracy and speed, it is designed to predict p... | [
"Gabriel Monteiro da Silva",
"Jennifer Y. Cui",
"David C. Dalgarno",
"George P. Lisi",
"Brenda M. Rubenstein"
] | [
"physics.bio-ph",
"physics.chem-ph",
"q-bio.BM"
] | [
"Medicine",
"Physics",
"Biology"
] | 2023-07-26T00:00:00 | https://arxiv.org/abs/2307.14470 | https://arxiv.org/pdf/2307.14470v1 | 2307.14470 | null | 7 | 0 | false | null | arXiv.org | 0.2258 |
b3b0ab95b1e40b071c2aebb956d54dc75c2f07564e335dfa5c473c89063e313b | [
"arxiv",
"semantic_scholar"
] | DeepGATGO: A Hierarchical Pretraining-Based Graph-Attention Model for Automatic Protein Function Prediction | Automatic protein function prediction (AFP) is classified as a large-scale multi-label classification problem aimed at automating protein enrichment analysis to eliminate the current reliance on labor-intensive wet-lab methods. Currently, popular methods primarily combine protein-related information and Gene Ontology (... | [
"Zihao Li",
"Changkun Jiang",
"Jianqiang Li"
] | [
"q-bio.QM",
"cs.LG"
] | [
"Computer Science",
"Biology"
] | 2023-07-24T00:00:00 | https://arxiv.org/abs/2307.13004 | https://arxiv.org/pdf/2307.13004v1 | 2307.13004 | 10.48550/arXiv.2307.13004 | 8 | 0 | false | null | arXiv.org | 0.2386 |
f3aab11a568876ce9f21e28c56cc7d916b42434cbed2ab7b91381f260de2dd75 | [
"arxiv",
"semantic_scholar"
] | A Zero-shot and Few-shot Study of Instruction-Finetuned Large Language Models Applied to Clinical and Biomedical Tasks | We evaluate four state-of-the-art instruction-tuned large language models (LLMs) -- ChatGPT, Flan-T5 UL2, Tk-Instruct, and Alpaca -- on a set of 13 real-world clinical and biomedical natural language processing (NLP) tasks in English, such as named-entity recognition (NER), question-answering (QA), relation extraction ... | [
"Yanis Labrak",
"Mickael Rouvier",
"Richard Dufour"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2023-07-22T00:00:00 | https://arxiv.org/abs/2307.12114 | https://arxiv.org/pdf/2307.12114v3 | 2307.12114 | 10.48550/arXiv.2307.12114 | 63 | 5 | false | null | International Conference on Language Resources and Evaluation | 0.4515 |
e09c4ec93ac0d80e35f185502401934500279dc4fd67d293757fcca9dbd9e961 | [
"arxiv",
"semantic_scholar"
] | Introduction to Protein Structure | While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previo... | [
"Annika Jacobsen",
"Erik van Dijk",
"Halima Mouhib",
"Bas Stringer",
"Olga Ivanova",
"Jose Gavaldá-Garciá",
"Laura Hoekstra",
"K. Anton Feenstra",
"Sanne Abeln"
] | [
"q-bio.BM"
] | [
"Biology"
] | 2023-07-05T00:00:00 | https://arxiv.org/abs/2307.02169 | https://arxiv.org/pdf/2307.02169v2 | 2307.02169 | null | 0 | 0 | false | null | null | 0 |
b67040e6728c6049ff97178b0f8d58e3edb2eda84dc343b385566d4b379b4c1f | [
"arxiv",
"semantic_scholar"
] | Monte Carlo for Protein Structures | While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previo... | [
"Juami H. M. van Gils",
"Maurits Dijkstra",
"Halima Mouhib",
"Arriën Symon Rauh",
"Jocelyne Vreede",
"K. Anton Feenstra",
"Sanne Abeln"
] | [
"q-bio.BM"
] | [
"Biology"
] | 2023-07-05T00:00:00 | https://arxiv.org/abs/2307.02177 | https://arxiv.org/pdf/2307.02177v2 | 2307.02177 | null | 0 | 0 | false | null | null | 0 |
8803f9c3723342877cd7b90741b917c21c128cf98987c89e96ad46ad28585d5b | [
"arxiv",
"semantic_scholar"
] | Introduction to Protein Folding | While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previo... | [
"Juami H. M. van Gils",
"Erik van Dijk",
"Ali May",
"Halima Mouhib",
"Jochem Bijlard",
"Annika Jacobsen",
"Isabel Houtkamp",
"K. Anton Feenstra",
"Sanne Abeln"
] | [
"q-bio.BM"
] | [
"Biology"
] | 2023-07-05T00:00:00 | https://arxiv.org/abs/2307.02174 | https://arxiv.org/pdf/2307.02174v2 | 2307.02174 | null | 0 | 0 | false | null | null | 0 |
0aa4313a168da4d589fcc11b16703456d3f1ee1dbe52620ac3b49dd933fc74ec | [
"arxiv",
"semantic_scholar"
] | Thermodynamics of Protein Folding | While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previo... | [
"Juami H. M. van Gils",
"Halima Mouhib",
"Erik van Dijk",
"Maurits Dijkstra",
"Isabel Houtkamp",
"Arthur Goetzee",
"Sanne Abeln",
"K. Anton Feenstra"
] | [
"q-bio.BM"
] | [
"Biology"
] | 2023-07-05T00:00:00 | https://arxiv.org/abs/2307.02175 | https://arxiv.org/pdf/2307.02175v2 | 2307.02175 | null | 0 | 0 | false | null | null | 0 |
822191eb0e475024e980d71ca2c9754eb00c498a83d38f5529a96b725354c8be | [
"arxiv",
"semantic_scholar"
] | Structural Property Prediction | While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previo... | [
"Maurits Dijkstra",
"Punto Bawono",
"Isabel Houtkamp",
"Jose Gavaldá-Garciá",
"Mascha Okounev",
"Robbin Bouwmeester",
"Bas Stringer",
"Jaap Heringa",
"Sanne Abeln",
"K. Anton Feenstra",
"Juami H. M. van Gils"
] | [
"q-bio.BM"
] | [
"Biology"
] | 2023-07-05T00:00:00 | https://arxiv.org/abs/2307.02172 | https://arxiv.org/pdf/2307.02172v2 | 2307.02172 | null | 0 | 0 | false | null | null | 0 |
29aaefc8e021637a00efd7fff11e81207b59d9f4d9aa4a02983744fc1ab594a2 | [
"arxiv",
"semantic_scholar"
] | ALBERTI, a Multilingual Domain Specific Language Model for Poetry Analysis | The computational analysis of poetry is limited by the scarcity of tools to automatically analyze and scan poems. In a multilingual settings, the problem is exacerbated as scansion and rhyme systems only exist for individual languages, making comparative studies very challenging and time consuming. In this work, we pre... | [
"Javier de la Rosa",
"Álvaro Pérez Pozo",
"Salvador Ros",
"Elena González-Blanco"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2023-07-03T00:00:00 | https://arxiv.org/abs/2307.01387 | https://arxiv.org/pdf/2307.01387v1 | 2307.01387 | 10.48550/arXiv.2307.01387 | 8 | 0 | false | null | null | 0.2386 |
c714534d49d494626999c2b5bef1b85eda6b63f907c37e0f72de4408c5c243a0 | [
"arxiv",
"semantic_scholar"
] | Protein-DNA binding sites prediction based on pre-trained protein language model and contrastive learning | Protein-DNA interaction is critical for life activities such as replication, transcription, and splicing. Identifying protein-DNA binding residues is essential for modeling their interaction and downstream studies. However, developing accurate and efficient computational methods for this task remains challenging. Impro... | [
"Yufan Liu",
"Boxue Tian"
] | [
"q-bio.BM",
"q-bio.QM"
] | [
"Computer Science",
"Medicine",
"Biology"
] | 2023-06-28T00:00:00 | https://arxiv.org/abs/2306.15912 | https://arxiv.org/pdf/2306.15912v1 | 2306.15912 | 10.1093/bib/bbad488 | 61 | 7 | true | https://github.com/YAndrewL/clape | null | 0.4515 |
f75160157dcd663bc2a9ff5731e270fa8bc08480d6ea61b4a875280fa8dff6d8 | [
"arxiv",
"semantic_scholar"
] | WizardCoder: Empowering Code Large Language Models with Evol-Instruct | Code Large Language Models (Code LLMs), such as StarCoder, have demonstrated exceptional performance in code-related tasks. However, most existing models are solely pre-trained on extensive raw code data without instruction fine-tuning. In this paper, we introduce WizardCoder, which empowers Code LLMs with complex inst... | [
"Ziyang Luo",
"Can Xu",
"Pu Zhao",
"Qingfeng Sun",
"Xiubo Geng",
"Wenxiang Hu",
"Chongyang Tao",
"Jing Ma",
"Qingwei Lin",
"Daxin Jiang"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2023-06-14T00:00:00 | https://arxiv.org/abs/2306.08568 | https://arxiv.org/pdf/2306.08568v2 | 2306.08568 | null | 977 | 121 | true | https://github.com/nlpxucan/WizardLM | International Conference on Learning Representations | 1 |
9db460d1af736c051401613b8615638ff279f21b69b4179ca548436666064249 | [
"arxiv",
"semantic_scholar"
] | A Comprehensive Review of State-of-The-Art Methods for Java Code Generation from Natural Language Text | Java Code Generation consists in generating automatically Java code from a Natural Language Text. This NLP task helps in increasing programmers' productivity by providing them with immediate solutions to the simplest and most repetitive tasks. Code generation is a challenging task because of the hard syntactic rules an... | [
"Jessica López Espejel",
"Mahaman Sanoussi Yahaya Alassan",
"El Mehdi Chouham",
"Walid Dahhane",
"El Hassane Ettifouri"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2023-06-10T00:00:00 | https://arxiv.org/abs/2306.06371 | https://arxiv.org/pdf/2306.06371v1 | 2306.06371 | 10.1016/j.nlp.2023.100013 | 17 | 0 | false | null | Natural Language Processing Journal | 0.3138 |
8071b56548c07b7216e56ef0bac05ee8b9de4ffdf5b5d21634663515949a078d | [
"arxiv",
"semantic_scholar"
] | Multi-level Protein Representation Learning for Blind Mutational Effect Prediction | Directed evolution plays an indispensable role in protein engineering that revises existing protein sequences to attain new or enhanced functions. Accurately predicting the effects of protein variants necessitates an in-depth understanding of protein structure and function. Although large self-supervised language model... | [
"Yang Tan",
"Bingxin Zhou",
"Yuanhong Jiang",
"Yu Guang Wang",
"Liang Hong"
] | [
"q-bio.QM",
"cs.AI"
] | [
"Computer Science",
"Biology"
] | 2023-06-08T00:00:00 | https://arxiv.org/abs/2306.04899 | https://arxiv.org/pdf/2306.04899v1 | 2306.04899 | 10.48550/arXiv.2306.04899 | 3 | 0 | false | null | arXiv.org | 0.1505 |
63c3cfddf3d9d87820a32183a90cf09eafbcb096448027be5e58cb8551352036 | [
"arxiv",
"semantic_scholar"
] | Enhancing the Protein Tertiary Structure Prediction by Multiple Sequence Alignment Generation | The field of protein folding research has been greatly advanced by deep learning methods, with AlphaFold2 (AF2) demonstrating exceptional performance and atomic-level precision. As co-evolution is integral to protein structure prediction, AF2's accuracy is significantly influenced by the depth of multiple sequence alig... | [
"Le Zhang",
"Jiayang Chen",
"Tao Shen",
"Yu Li",
"Siqi Sun"
] | [
"q-bio.QM",
"cs.CE",
"cs.LG",
"q-bio.BM"
] | [
"Biology",
"Computer Science"
] | 2023-06-02T00:00:00 | https://arxiv.org/abs/2306.01824 | https://arxiv.org/pdf/2306.01824v1 | 2306.01824 | 10.48550/arXiv.2306.01824 | 10 | 1 | false | null | arXiv.org | 0.2603 |
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