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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
826208b068406d914385cecfe2da016183938b219b28a5e1e965aff0d675800f | [
"arxiv",
"semantic_scholar"
] | AF2-Mutation: Adversarial Sequence Mutations against AlphaFold2 on Protein Tertiary Structure Prediction | Deep learning-based approaches, such as AlphaFold2 (AF2), have significantly advanced protein tertiary structure prediction, achieving results comparable to real biological experimental methods. While AF2 has shown limitations in predicting the effects of mutations, its robustness against sequence mutations remains to ... | [
"Zhongju Yuan",
"Tao Shen",
"Sheng Xu",
"Leiye Yu",
"Ruobing Ren",
"Siqi Sun"
] | [
"q-bio.BM",
"cs.AI",
"cs.LG"
] | [
"Computer Science",
"Biology"
] | 2023-05-15T00:00:00 | https://arxiv.org/abs/2305.08929 | https://arxiv.org/pdf/2305.08929v1 | 2305.08929 | 10.15212/AMM-2024-0047 | 3 | 0 | false | null | Acta Materia Medica | 0.1505 |
ca66abe7fd1aecb6eef2a36463c4ba909d3ab02c2865600c8f029e17545e799e | [
"arxiv",
"semantic_scholar"
] | A Latent Diffusion Model for Protein Structure Generation | Proteins are complex biomolecules that perform a variety of crucial functions within living organisms. Designing and generating novel proteins can pave the way for many future synthetic biology applications, including drug discovery. However, it remains a challenging computational task due to the large modeling space o... | [
"Cong Fu",
"Keqiang Yan",
"Limei Wang",
"Wing Yee Au",
"Michael McThrow",
"Tao Komikado",
"Koji Maruhashi",
"Kanji Uchino",
"Xiaoning Qian",
"Shuiwang Ji"
] | [
"q-bio.BM",
"cs.AI",
"cs.LG"
] | [
"Biology",
"Computer Science"
] | 2023-05-06T00:00:00 | https://arxiv.org/abs/2305.04120 | https://arxiv.org/pdf/2305.04120v2 | 2305.04120 | 10.48550/arXiv.2305.04120 | 53 | 6 | true | https://github.com/divelab/AIRS/tree/main/OpenProt/LatentDiff | LOG IN | 0.4331 |
5e658926c61771765f5f8a4b540e071e33e1584a0f9082dc6201cce81957c39f | [
"arxiv",
"semantic_scholar"
] | WizardLM: Empowering large pre-trained language models to follow complex instructions | Training large language models (LLMs) with open-domain instruction following data brings colossal success. However, manually creating such instruction data is very time-consuming and labor-intensive. Moreover, humans may struggle to produce high-complexity instructions. In this paper, we show an avenue for creating lar... | [
"Can Xu",
"Qingfeng Sun",
"Kai Zheng",
"Xiubo Geng",
"Pu Zhao",
"Jiazhan Feng",
"Chongyang Tao",
"Qingwei Lin",
"Daxin Jiang"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2023-04-24T00:00:00 | https://arxiv.org/abs/2304.12244 | https://arxiv.org/pdf/2304.12244v3 | 2304.12244 | null | 1,242 | 157 | true | https://github.com/nlpxucan/WizardLM | International Conference on Learning Representations | 1 |
0ed196163285eab0467f7a0d998ecc47a8db0eca79106e7654b36e96ef588789 | [
"arxiv",
"semantic_scholar"
] | Learning to Plan with Natural Language | Large Language Models (LLMs) have shown remarkable performance in various basic natural language tasks. For completing the complex task, we still need a plan for the task to guide LLMs to generate the specific solutions step by step. LLMs can directly generate task plans, but these plans may still contain factual error... | [
"Yiduo Guo",
"Yaobo Liang",
"Chenfei Wu",
"Wenshan Wu",
"Dongyan Zhao",
"Nan Duan"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2023-04-20T00:00:00 | https://arxiv.org/abs/2304.10464 | https://arxiv.org/pdf/2304.10464v4 | 2304.10464 | 10.18653/v1/2024.findings-emnlp.589 | 6 | 0 | true | https://github.com/Eureka6174/LearnNLPlan} | Conference on Empirical Methods in Natural Language Processing | 0.2113 |
d91dab8c48e77153416e842c12a9f91acf7a94b2d2814c97d8a50815c3bdffa8 | [
"arxiv",
"semantic_scholar"
] | CodeKGC: Code Language Model for Generative Knowledge Graph Construction | Current generative knowledge graph construction approaches usually fail to capture structural knowledge by simply flattening natural language into serialized texts or a specification language. However, large generative language model trained on structured data such as code has demonstrated impressive capability in unde... | [
"Zhen Bi",
"Jing Chen",
"Yinuo Jiang",
"Feiyu Xiong",
"Wei Guo",
"Huajun Chen",
"Ningyu Zhang"
] | [
"cs.CL",
"cs.AI",
"cs.IR",
"cs.LG",
"cs.SE"
] | [
"Computer Science"
] | 2023-04-18T00:00:00 | https://arxiv.org/abs/2304.09048 | https://arxiv.org/pdf/2304.09048v2 | 2304.09048 | 10.1145/3641850 | 79 | 1 | true | https://github.com/zjunlp/DeepKE/tree/main/example/llm | null | 0.4758 |
8e730bc2f932cdbe012acb9476304cc107bfaf3f1d08784b10395d3a91cf0771 | [
"arxiv",
"semantic_scholar"
] | TemPL: A Novel Deep Learning Model for Zero-Shot Prediction of Protein Stability and Activity Based on Temperature-Guided Language Modeling | We introduce TemPL, a novel deep learning approach for zero-shot prediction of protein stability and activity, harnessing temperature-guided language modeling. By assembling an extensive dataset of 96 million sequence-host bacterial strain optimal growth temperatures (OGTs) and ΔTm data for point mutations under consis... | [
"Pan Tan",
"Mingchen Li",
"Liang Zhang",
"Zhiqiang Hu",
"Liang Hong"
] | [
"q-bio.QM"
] | [
"Biology"
] | 2023-04-07T00:00:00 | https://arxiv.org/abs/2304.03780 | https://arxiv.org/pdf/2304.03780v5 | 2304.03780 | null | 1 | 0 | false | null | null | 0.0753 |
857a4a4ac4a085a6807b455b474a9dce1e8cdda74ab273b84807156e790b8bc4 | [
"arxiv",
"semantic_scholar"
] | EigenFold: Generative Protein Structure Prediction with Diffusion Models | Protein structure prediction has reached revolutionary levels of accuracy on single structures, yet distributional modeling paradigms are needed to capture the conformational ensembles and flexibility that underlie biological function. Towards this goal, we develop EigenFold, a diffusion generative modeling framework f... | [
"Bowen Jing",
"Ezra Erives",
"Peter Pao-Huang",
"Gabriele Corso",
"Bonnie Berger",
"Tommi Jaakkola"
] | [
"q-bio.BM",
"cs.LG",
"physics.bio-ph"
] | [
"Medicine",
"Computer Science",
"Biology",
"Physics"
] | 2023-04-05T00:00:00 | https://arxiv.org/abs/2304.02198 | https://arxiv.org/pdf/2304.02198v1 | 2304.02198 | 10.48550/arXiv.2304.02198 | 113 | 8 | true | https://github.com/bjing2016/EigenFold | arXiv.org | 0.5142 |
2dd310656c8fb3999ce1aaf4b7290e0a58c6fafbe028bf8144cad36411718ed9 | [
"arxiv",
"semantic_scholar"
] | ProtFIM: Fill-in-Middle Protein Sequence Design via Protein Language Models | Protein language models (pLMs), pre-trained via causal language modeling on protein sequences, have been a promising tool for protein sequence design. In real-world protein engineering, there are many cases where the amino acids in the middle of a protein sequence are optimized while maintaining other residues. Unfortu... | [
"Youhan Lee",
"Hasun Yu"
] | [
"cs.LG",
"cs.AI",
"q-bio.BM"
] | [
"Computer Science",
"Biology"
] | 2023-03-29T00:00:00 | https://arxiv.org/abs/2303.16452 | https://arxiv.org/pdf/2303.16452v1 | 2303.16452 | 10.48550/arXiv.2303.16452 | 3 | 0 | false | null | arXiv.org | 0.1505 |
915e222b61c0bef59e854d3fd62e4291518687154163da3e2ec16d7959482f1b | [
"arxiv",
"semantic_scholar"
] | A Systematic Study of Joint Representation Learning on Protein Sequences and Structures | Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein functions. Recent sequence representation learning methods based on Protein Language Models (PLMs) excel in sequence-based tasks, but their direct adaptation to tasks involving protein structures remains a... | [
"Zuobai Zhang",
"Chuanrui Wang",
"Minghao Xu",
"Vijil Chenthamarakshan",
"Aurélie Lozano",
"Payel Das",
"Jian Tang"
] | [
"q-bio.QM",
"cs.LG"
] | [
"Biology",
"Computer Science"
] | 2023-03-11T00:00:00 | https://arxiv.org/abs/2303.06275 | https://arxiv.org/pdf/2303.06275v2 | 2303.06275 | null | 55 | 2 | true | https://github.com/DeepGraphLearning/ESM-GearNet | null | 0.437 |
265864d7ea56e774037ffd50ec4cdd100527f4b142b37cf6a55cc23f531d3ab6 | [
"arxiv",
"semantic_scholar"
] | Internal-Coordinate Density Modelling of Protein Structure: Covariance Matters | After the recent ground-breaking advances in protein structure prediction, one of the remaining challenges in protein machine learning is to reliably predict distributions of structural states. Parametric models of fluctuations are difficult to fit due to complex covariance structures between degrees of freedom in the ... | [
"Marloes Arts",
"Jes Frellsen",
"Wouter Boomsma"
] | [
"cs.LG",
"q-bio.BM"
] | [
"Computer Science",
"Biology"
] | 2023-02-27T00:00:00 | https://arxiv.org/abs/2302.13711 | https://arxiv.org/pdf/2302.13711v3 | 2302.13711 | 10.48550/arXiv.2302.13711 | 2 | 0 | false | null | null | 0.1193 |
e711c63d02730493c9bccf6ebcd9a4514e362de50633fb6e19e0b0a613ddccc2 | [
"arxiv",
"semantic_scholar"
] | Retrieved Sequence Augmentation for Protein Representation Learning | Protein language models have excelled in a variety of tasks, ranging from structure prediction to protein engineering. However, proteins are highly diverse in functions and structures, and current state-of-the-art models including the latest version of AlphaFold rely on Multiple Sequence Alignments (MSA) to feed in the... | [
"Chang Ma",
"Haiteng Zhao",
"Lin Zheng",
"Jiayi Xin",
"Qintong Li",
"Lijun Wu",
"Zhihong Deng",
"Yang Lu",
"Qi Liu",
"Lingpeng Kong"
] | [
"q-bio.BM",
"cs.LG"
] | [
"Computer Science",
"Biology"
] | 2023-02-24T00:00:00 | https://arxiv.org/abs/2302.12563 | https://arxiv.org/pdf/2302.12563v1 | 2302.12563 | 10.1101/2023.02.22.529597 | 16 | 0 | true | https://github.com/HKUNLP/RSA | bioRxiv | 0.3076 |
332cd44edf4a08a6edb142e70bbfa5061a9e02b59af2dfdcdc8282dacfe0378f | [
"arxiv",
"semantic_scholar"
] | Semantic Importance-Aware Communications Using Pre-trained Language Models | This letter proposes a semantic importance-aware communication (SIAC) scheme using pre-trained language models (e.g., ChatGPT, BERT, etc.). Specifically, we propose a cross-layer design with a pre-trained language model embedded in/connected by the cross-layer manager. The pre-trained language model is utilized to quan... | [
"Shuaishuai Guo",
"Yanhu Wang",
"Shujing Li",
"Nasir Saeed"
] | [
"eess.SP"
] | [
"Engineering",
"Computer Science"
] | 2023-02-12T00:00:00 | https://arxiv.org/abs/2302.07142 | https://arxiv.org/pdf/2302.07142v2 | 2302.07142 | 10.1109/LCOMM.2023.3293805 | 72 | 1 | false | null | IEEE Communications Letters | 0.4658 |
351e7cd29bedcfe9e36e020f68c303011cc060dca1e0bcf48418d1fdc335610e | [
"arxiv",
"semantic_scholar"
] | NodeCoder: a graph-based machine learning platform to predict active sites of modeled protein structures | While accurate protein structure predictions are now available for nearly every observed protein sequence, predicted structures lack much of the functional context offered by experimental structure determination. We address this gap with NodeCoder, a task-independent platform that maps residue-based datasets onto 3D pr... | [
"Nasim Abdollahi",
"Seyed Ali Madani Tonekaboni",
"Jay Huang",
"Bo Wang",
"Stephen MacKinnon"
] | [
"q-bio.QM"
] | [
"Biology"
] | 2023-02-07T00:00:00 | https://arxiv.org/abs/2302.03590 | https://arxiv.org/pdf/2302.03590v1 | 2302.03590 | null | 12 | 0 | true | null | null | 0.2785 |
37c266d9e09e2439b108e4d14fc54ecc89eba6d781351cd51062cfddd7e323df | [
"arxiv",
"semantic_scholar"
] | Structure-informed Language Models Are Protein Designers | This paper demonstrates that language models are strong structure-based protein designers. We present LM-Design, a generic approach to reprogramming sequence-based protein language models (pLMs), that have learned massive sequential evolutionary knowledge from the universe of natural protein sequences, to acquire an im... | [
"Zaixiang Zheng",
"Yifan Deng",
"Dongyu Xue",
"Yi Zhou",
"Fei YE",
"Quanquan Gu"
] | [
"cs.LG"
] | [
"Computer Science",
"Biology"
] | 2023-02-03T00:00:00 | https://arxiv.org/abs/2302.01649 | https://arxiv.org/pdf/2302.01649v2 | 2302.01649 | 10.1101/2023.02.03.526917 | 135 | 18 | false | null | bioRxiv | 0.6394 |
1a03220774dedc43bd6a335c63763e0b440f4b53783242963ef4211bee9c7e61 | [
"arxiv",
"semantic_scholar"
] | ExplainableFold: Understanding AlphaFold Prediction with Explainable AI | This paper presents ExplainableFold, an explainable AI framework for protein structure prediction. Despite the success of AI-based methods such as AlphaFold in this field, the underlying reasons for their predictions remain unclear due to the black-box nature of deep learning models. To address this, we propose a count... | [
"Juntao Tan",
"Yongfeng Zhang"
] | [
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2023-01-27T00:00:00 | https://arxiv.org/abs/2301.11765 | https://arxiv.org/pdf/2301.11765v2 | 2301.11765 | 10.1145/3580305.3599337 | 17 | 0 | false | null | Knowledge Discovery and Data Mining | 0.3138 |
9a3dfa7c8dff84582d41500dae10b4885994cbad359cbcb6d45a2f144d971f03 | [
"arxiv",
"semantic_scholar"
] | DiffSDS: A language diffusion model for protein backbone inpainting under geometric conditions and constraints | Have you ever been troubled by the complexity and computational cost of SE(3) protein structure modeling and been amazed by the simplicity and power of language modeling? Recent work has shown promise in simplifying protein structures as sequences of protein angles; therefore, language models could be used for unconstr... | [
"Zhangyang Gao",
"Cheng Tan",
"Stan Z. Li"
] | [
"q-bio.QM",
"cs.AI",
"cs.LG"
] | [
"Biology",
"Computer Science"
] | 2023-01-22T00:00:00 | https://arxiv.org/abs/2301.09642 | https://arxiv.org/pdf/2301.09642v1 | 2301.09642 | 10.48550/arXiv.2301.09642 | 23 | 0 | false | null | arXiv.org | 0.3451 |
8d5732ad139fa97e20570796913359595778cd656df554dd79b7d96f76376d7c | [
"arxiv",
"semantic_scholar"
] | Beating the Best: Improving on AlphaFold2 at Protein Structure Prediction | The goal of Protein Structure Prediction (PSP) problem is to predict a protein's 3D structure (confirmation) from its amino acid sequence. The problem has been a 'holy grail' of science since the Noble prize-winning work of Anfinsen demonstrated that protein conformation was determined by sequence. A recent and importa... | [
"Abbi Abdel-Rehim",
"Oghenejokpeme Orhobor",
"Hang Lou",
"Hao Ni",
"Ross D. King"
] | [
"q-bio.BM",
"cs.LG"
] | [
"Computer Science",
"Biology"
] | 2023-01-18T00:00:00 | https://arxiv.org/abs/2301.07568 | https://arxiv.org/pdf/2301.07568v2 | 2301.07568 | 10.48550/arXiv.2301.07568 | 2 | 0 | false | null | arXiv.org | 0.1193 |
a0c86642b0e6fe1acba01927179c2770a0f3882b5f4fe8764d543f3b58a73767 | [
"arxiv",
"semantic_scholar"
] | Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling | As opposed to scaling-up protein language models (PLMs), we seek improving performance via protein-specific optimization. Although the proportionality between the language model size and the richness of its learned representations is validated, we prioritize accessibility and pursue a path of data-efficient, cost-reduc... | [
"Ahmed Elnaggar",
"Hazem Essam",
"Wafaa Salah-Eldin",
"Walid Moustafa",
"Mohamed Elkerdawy",
"Charlotte Rochereau",
"Burkhard Rost"
] | [
"cs.LG",
"cs.CL",
"cs.DC",
"q-bio.QM"
] | [
"Computer Science",
"Biology"
] | 2023-01-16T00:00:00 | https://arxiv.org/abs/2301.06568 | https://arxiv.org/pdf/2301.06568v1 | 2301.06568 | 10.1101/2023.01.16.524265 | 164 | 20 | false | null | bioRxiv | 0.6611 |
575636c973e45a311c58f9bcec1f8853f7e94918eff7795368104aeb715e43e6 | [
"arxiv",
"semantic_scholar"
] | Language Cognition and Language Computation -- Human and Machine Language Understanding | Language understanding is a key scientific issue in the fields of cognitive and computer science. However, the two disciplines differ substantially in the specific research questions. Cognitive science focuses on analyzing the specific mechanism of the brain and investigating the brain's response to language; few studi... | [
"Shaonan Wang",
"Nai Ding",
"Nan Lin",
"Jiajun Zhang",
"Chengqing Zong"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2023-01-12T00:00:00 | https://arxiv.org/abs/2301.04788 | https://arxiv.org/pdf/2301.04788v1 | 2301.04788 | 10.48550/arXiv.2301.04788 | 2 | 0 | false | null | arXiv.org | 0.1193 |
953e7e550921c206f7c2c18ac0082d82baeaa66969cbdc7f28b88ce0b9a59774 | [
"arxiv",
"semantic_scholar"
] | On the Robustness of AlphaFold: A COVID-19 Case Study | Protein folding neural networks (PFNNs) such as AlphaFold predict remarkably accurate structures of proteins compared to other approaches. However, the robustness of such networks has heretofore not been explored. This is particularly relevant given the broad social implications of such technologies and the fact that b... | [
"Ismail Alkhouri",
"Sumit Jha",
"Andre Beckus",
"George Atia",
"Alvaro Velasquez",
"Rickard Ewetz",
"Arvind Ramanathan",
"Susmit Jha"
] | [
"cs.LG",
"q-bio.BM"
] | [
"Computer Science",
"Biology"
] | 2023-01-10T00:00:00 | https://arxiv.org/abs/2301.04093 | https://arxiv.org/pdf/2301.04093v2 | 2301.04093 | 10.48550/arXiv.2301.04093 | 5 | 0 | false | null | arXiv.org | 0.1945 |
928f3f4525621e5db803a031ef35d62857b6857ad121282b651261ce42314057 | [
"arxiv",
"semantic_scholar"
] | Reprogramming Pretrained Language Models for Protein Sequence Representation Learning | Machine Learning-guided solutions for protein learning tasks have made significant headway in recent years. However, success in scientific discovery tasks is limited by the accessibility of well-defined and labeled in-domain data. To tackle the low-data constraint, recent adaptions of deep learning models pretrained on... | [
"Ria Vinod",
"Pin-Yu Chen",
"Payel Das"
] | [
"cs.LG",
"cs.CL",
"q-bio.BM"
] | [
"Computer Science",
"Biology"
] | 2023-01-05T00:00:00 | https://arxiv.org/abs/2301.02120 | https://arxiv.org/pdf/2301.02120v1 | 2301.02120 | 10.48550/arXiv.2301.02120 | 15 | 2 | false | null | Digital Discovery | 0.301 |
a2050f76f86ba35126c48e4a7275cf589261d7d749ce50a1fac194d248e0eebf | [
"arxiv",
"semantic_scholar"
] | Protein Structure Prediction until CASP15 | In Dec 2020, the results of AlphaFold2 were presented at CASP14, sparking a revolution in the field of protein structure predictions. For the first time, a purely computational method could challenge experimental accuracy for structure prediction of single protein domains. The code of AlphaFold2 was released in the sum... | [
"Arne Elofsson"
] | [
"q-bio.BM"
] | [
"Biology"
] | 2022-12-15T00:00:00 | https://arxiv.org/abs/2212.07702 | https://arxiv.org/pdf/2212.07702v1 | 2212.07702 | null | 2 | 1 | false | null | null | 0.1505 |
f6a47fd22a360d472371af672a4dd292223f8b1f700f20e1a559e343b5b68308 | [
"arxiv",
"semantic_scholar"
] | Prompting Is Programming: A Query Language for Large Language Models | Large language models have demonstrated outstanding performance on a wide range of tasks such as question answering and code generation. On a high level, given an input, a language model can be used to automatically complete the sequence in a statistically-likely way. Based on this, users prompt these models with langu... | [
"Luca Beurer-Kellner",
"Marc Fischer",
"Martin Vechev"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2022-12-12T00:00:00 | https://arxiv.org/abs/2212.06094 | https://arxiv.org/pdf/2212.06094v3 | 2212.06094 | 10.1145/3591300 | 185 | 9 | false | null | null | 0.5674 |
8554e1c04943e8622214fd3d8a51082be48fd5911202bbcbeeaccace9ab7fcc3 | [
"arxiv",
"semantic_scholar"
] | Integration of Pre-trained Protein Language Models into Geometric Deep Learning Networks | Geometric deep learning has recently achieved great success in non-Euclidean domains, and learning on 3D structures of large biomolecules is emerging as a distinct research area. However, its efficacy is largely constrained due to the limited quantity of structural data. Meanwhile, protein language models trained on su... | [
"Fang Wu",
"Lirong Wu",
"Dragomir Radev",
"Jinbo Xu",
"Stan Z. Li"
] | [
"cs.LG",
"cs.CE",
"q-bio.QM"
] | [
"Medicine",
"Computer Science",
"Biology"
] | 2022-12-07T00:00:00 | https://arxiv.org/abs/2212.03447 | https://arxiv.org/pdf/2212.03447v2 | 2212.03447 | 10.1038/s42003-023-05133-1 | 57 | 4 | false | null | Communications Biology | 0.4409 |
2e6d2e27a38c18fc2ef05ab6864b2d28c1370cd326e316afef3112731963912e | [
"arxiv",
"semantic_scholar"
] | SOLD: Sinhala Offensive Language Dataset | The widespread of offensive content online, such as hate speech and cyber-bullying, is a global phenomenon. This has sparked interest in the artificial intelligence (AI) and natural language processing (NLP) communities, motivating the development of various systems trained to detect potentially harmful content automat... | [
"Tharindu Ranasinghe",
"Isuri Anuradha",
"Damith Premasiri",
"Kanishka Silva",
"Hansi Hettiarachchi",
"Lasitha Uyangodage",
"Marcos Zampieri"
] | [
"cs.CL",
"cs.AI",
"cs.LG",
"cs.SI"
] | [
"Computer Science"
] | 2022-12-01T00:00:00 | https://arxiv.org/abs/2212.00851 | https://arxiv.org/pdf/2212.00851v2 | 2212.00851 | 10.1007/s10579-024-09723-1 | 17 | 0 | false | null | Language Resources and Evaluation | 0.3138 |
5ae73fbfbaf7540d294d33e044e1ccaf4312a5f11a528739894023a584701e35 | [
"arxiv",
"semantic_scholar"
] | Protein Language Models and Structure Prediction: Connection and Progression | The prediction of protein structures from sequences is an important task for function prediction, drug design, and related biological processes understanding. Recent advances have proved the power of language models (LMs) in processing the protein sequence databases, which inherit the advantages of attention networks a... | [
"Bozhen Hu",
"Jun Xia",
"Jiangbin Zheng",
"Cheng Tan",
"Yufei Huang",
"Yongjie Xu",
"Stan Z. Li"
] | [
"q-bio.QM",
"cs.AI",
"cs.LG"
] | [
"Computer Science",
"Biology"
] | 2022-11-30T00:00:00 | https://arxiv.org/abs/2211.16742 | https://arxiv.org/pdf/2211.16742v1 | 2211.16742 | 10.48550/arXiv.2211.16742 | 48 | 1 | false | null | arXiv.org | 0.4225 |
a56754ee70f93f68585f5d2416bd38792228ba9c3fc24f1eff8db4d3dadbecb3 | [
"arxiv",
"semantic_scholar"
] | An Overview of Indian Spoken Language Recognition from Machine Learning Perspective | Automatic spoken language identification (LID) is a very important research field in the era of multilingual voice-command-based human-computer interaction (HCI). A front-end LID module helps to improve the performance of many speech-based applications in the multilingual scenario. India is a populous country with dive... | [
"Spandan Dey",
"Md Sahidullah",
"Goutam Saha"
] | [
"cs.CL",
"cs.SD",
"eess.AS"
] | [
"Computer Science",
"Engineering"
] | 2022-11-30T00:00:00 | https://arxiv.org/abs/2212.03812 | https://arxiv.org/pdf/2212.03812v1 | 2212.03812 | 10.1145/3523179 | 38 | 1 | false | null | ACM Transactions on Asian and Low-Resource Language Information Processing, Volume 21, Issue 6 November 2022, Article No 128 | 0.3978 |
b9228f7f478df02b4b96c0d3ea6880d2d7a0e81ff75eebc59f812b450d6f8fad | [
"arxiv",
"semantic_scholar"
] | Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction | A central problem in computational biophysics is protein structure prediction, i.e., finding the optimal folding of a given amino acid sequence. This problem has been studied in a classical abstract model, the HP model, where the protein is modeled as a sequence of H (hydrophobic) and P (polar) amino acids on a lattice... | [
"Kaiyuan Yang",
"Houjing Huang",
"Olafs Vandans",
"Adithya Murali",
"Fujia Tian",
"Roland H. C. Yap",
"Liang Dai"
] | [
"cs.LG",
"q-bio.BM"
] | [
"Computer Science",
"Biology"
] | 2022-11-27T00:00:00 | https://arxiv.org/abs/2211.14939 | https://arxiv.org/pdf/2211.14939v2 | 2211.14939 | 10.1016/j.physa.2022.128395 | 14 | 1 | false | null | null | 0.294 |
b9a2b2e3d509350fb9f5331a15305d87b1d032893a9d1e34c5f5f8682b99f543 | [
"arxiv",
"semantic_scholar"
] | Protein language model rescue mutations highlight variant effects and structure in clinically relevant genes | Despite being self-supervised, protein language models have shown remarkable performance in fundamental biological tasks such as predicting impact of genetic variation on protein structure and function. The effectiveness of these models on diverse set of tasks suggests that they learn meaningful representations of fitn... | [
"Onuralp Soylemez",
"Pablo Cordero"
] | [
"cs.LG",
"q-bio.GN"
] | [
"Computer Science",
"Biology"
] | 2022-11-18T00:00:00 | https://arxiv.org/abs/2211.10000 | https://arxiv.org/pdf/2211.10000v1 | 2211.10000 | 10.48550/arXiv.2211.10000 | 0 | 0 | false | null | arXiv.org | 0 |
b3fdc3af0edaf9bf6983d3aaaf40e26128a215450371297214352fb232c3b4e1 | [
"arxiv",
"semantic_scholar"
] | Radiomics-enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck Cancer | Outcome prediction is crucial for head and neck cancer patients as it can provide prognostic information for early treatment planning. Radiomics methods have been widely used for outcome prediction from medical images. However, these methods are limited by their reliance on intractable manual segmentation of tumor regi... | [
"Mingyuan Meng",
"Lei Bi",
"Dagan Feng",
"Jinman Kim"
] | [
"eess.IV",
"cs.CV",
"cs.LG"
] | [
"Computer Science",
"Engineering"
] | 2022-11-10T00:00:00 | https://arxiv.org/abs/2211.05409 | https://arxiv.org/pdf/2211.05409v1 | 2211.05409 | 10.1007/978-3-031-27420-6_14 | 20 | 2 | false | null | Head and Neck Tumor Segmentation and Outcome Prediction (HECKTOR 2022), pp.135-143 | 0.3306 |
8027e5dae72ae373ff3ff3eac87cd7f973aa54cf2fd4a1b4b019dfe87a4adfab | [
"arxiv",
"semantic_scholar"
] | AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages | In recent years, multilingual pre-trained language models have gained prominence due to their remarkable performance on numerous downstream Natural Language Processing tasks (NLP). However, pre-training these large multilingual language models requires a lot of training data, which is not available for African Language... | [
"Bonaventure F. P. Dossou",
"Atnafu Lambebo Tonja",
"Oreen Yousuf",
"Salomey Osei",
"Abigail Oppong",
"Iyanuoluwa Shode",
"Oluwabusayo Olufunke Awoyomi",
"Chris Chinenye Emezue"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2022-11-07T00:00:00 | https://arxiv.org/abs/2211.03263 | https://arxiv.org/pdf/2211.03263v2 | 2211.03263 | 10.48550/arXiv.2211.03263 | 68 | 7 | true | https://github.com/bonaventuredossou/MLM_AL | null | 0.4597 |
1fe2bea2342f79b2ae649d34ef8d0ce3991dc5a64ebe2eee570334eb25f78125 | [
"arxiv",
"semantic_scholar"
] | An Efficient MCMC Approach to Energy Function Optimization in Protein Structure Prediction | Protein structure prediction is a critical problem linked to drug design, mutation detection, and protein synthesis, among other applications. To this end, evolutionary data has been used to build contact maps which are traditionally minimized as energy functions via gradient descent based schemes like the L-BFGS algor... | [
"Lakshmi A. Ghantasala",
"Risi Jaiswal",
"Supriyo Datta"
] | [
"q-bio.BM",
"q-bio.QM",
"stat.CO"
] | [
"Biology",
"Mathematics"
] | 2022-11-06T00:00:00 | https://arxiv.org/abs/2211.03193 | https://arxiv.org/pdf/2211.03193v1 | 2211.03193 | null | 0 | 0 | false | null | null | 0 |
66e98fba6657123729e6031fe5592c346fb3e218530283766a1e6cfb985321ec | [
"arxiv",
"semantic_scholar"
] | Autoregressive Structured Prediction with Language Models | Recent years have seen a paradigm shift in NLP towards using pretrained language models ({PLM}) for a wide range of tasks. However, there are many difficult design decisions to represent structures (e.g. tagged text, coreference chains) in a way such that they can be captured by PLMs. Prior work on structured predictio... | [
"Tianyu Liu",
"Yuchen Jiang",
"Nicholas Monath",
"Ryan Cotterell",
"Mrinmaya Sachan"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2022-10-26T00:00:00 | https://arxiv.org/abs/2210.14698 | https://arxiv.org/pdf/2210.14698v2 | 2210.14698 | 10.48550/arXiv.2210.14698 | 67 | 8 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.4771 |
7c1eb008433700fce8ab94972e924dd55c24f2a227dab29a7a14cae5c69b996c | [
"arxiv",
"semantic_scholar"
] | AlphaFold Distillation for Protein Design | Inverse protein folding, the process of designing sequences that fold into a specific 3D structure, is crucial in bio-engineering and drug discovery. Traditional methods rely on experimentally resolved structures, but these cover only a small fraction of protein sequences. Forward folding models like AlphaFold offer a ... | [
"Igor Melnyk",
"Aurelie Lozano",
"Payel Das",
"Vijil Chenthamarakshan"
] | [
"q-bio.BM",
"cs.LG"
] | [
"Biology",
"Computer Science"
] | 2022-10-05T00:00:00 | https://arxiv.org/abs/2210.03488 | https://arxiv.org/pdf/2210.03488v2 | 2210.03488 | null | 1 | 0 | true | https://github.com/IBM/AFDistill | null | 0.0753 |
2b72f59ff4c5089a88f1c8b82c101ffc6b6d4d161d2be6003bcb104a729d4f7c | [
"arxiv",
"semantic_scholar"
] | State-specific protein-ligand complex structure prediction with a multi-scale deep generative model | The binding complexes formed by proteins and small molecule ligands are ubiquitous and critical to life. Despite recent advancements in protein structure prediction, existing algorithms are so far unable to systematically predict the binding ligand structures along with their regulatory effects on protein folding. To a... | [
"Zhuoran Qiao",
"Weili Nie",
"Arash Vahdat",
"Thomas F. Miller",
"Anima Anandkumar"
] | [
"q-bio.QM",
"cs.LG",
"q-bio.BM"
] | [
"Biology",
"Computer Science"
] | 2022-09-30T00:00:00 | https://arxiv.org/abs/2209.15171 | https://arxiv.org/pdf/2209.15171v2 | 2209.15171 | 10.1038/s42256-024-00792-z | 169 | 11 | false | null | Nature Machine Intelligence | 0.5576 |
caa7f07ef1d6135be75f8c56eda3e5217f6a29c2530fafe4f6f16ca552f260c2 | [
"arxiv",
"semantic_scholar"
] | Secondary Protein Structure Prediction Using Neural Networks | In this paper we experiment with using neural network structures to predict a protein's secondary structure (α helix positions) from only its primary structure (amino acid sequence). We implement a fully connected neural network (FCNN) and preform three experiments using that FCNN. Firstly, we do a cross-species compar... | [
"Sidharth Malhotra",
"Robin Walters"
] | [
"cs.LG",
"q-bio.QM"
] | [
"Computer Science",
"Biology"
] | 2022-08-24T00:00:00 | https://arxiv.org/abs/2208.11248 | https://arxiv.org/pdf/2208.11248v1 | 2208.11248 | 10.48550/arXiv.2208.11248 | 2 | 0 | false | null | arXiv.org | 0.1193 |
4f78825126c131154fd8aca23ba41f9de2155fe52d5e9c3b3f22d9abd682ef79 | [
"arxiv",
"semantic_scholar"
] | HelixFold-Single: MSA-free Protein Structure Prediction by Using Protein Language Model as an Alternative | AI-based protein structure prediction pipelines, such as AlphaFold2, have achieved near-experimental accuracy. These advanced pipelines mainly rely on Multiple Sequence Alignments (MSAs) as inputs to learn the co-evolution information from the homologous sequences. Nonetheless, searching MSAs from protein databases is ... | [
"Xiaomin Fang",
"Fan Wang",
"Lihang Liu",
"Jingzhou He",
"Dayong Lin",
"Yingfei Xiang",
"Xiaonan Zhang",
"Hua Wu",
"Hui Li",
"Le Song"
] | [
"q-bio.BM",
"cs.AI",
"cs.LG",
"q-bio.QM"
] | [
"Computer Science",
"Biology"
] | 2022-07-28T00:00:00 | https://arxiv.org/abs/2207.13921 | https://arxiv.org/pdf/2207.13921v3 | 2207.13921 | 10.1038/s42256-023-00721-6 | 86 | 3 | true | https://github.com/PaddlePaddle/PaddleHelix/tree/dev/apps/protein_folding/helixfold-single | Nature Machine Intelligence | 0.4849 |
2bb21b2f708c23272a1c18ea295e5c5a1aa7b94533f2d5c360474eae18905564 | [
"arxiv",
"semantic_scholar"
] | End-to-End Spoken Language Understanding: Performance analyses of a voice command task in a low resource setting | Spoken Language Understanding (SLU) is a core task in most human-machine interaction systems. With the emergence of smart homes, smart phones and smart speakers, SLU has become a key technology for the industry. In a classical SLU approach, an Automatic Speech Recognition (ASR) module transcribes the speech signal into... | [
"Thierry Desot",
"François Portet",
"Michel Vacher"
] | [
"cs.CL",
"cs.SD",
"eess.AS"
] | [
"Computer Science",
"Engineering"
] | 2022-07-17T00:00:00 | https://arxiv.org/abs/2207.08179 | https://arxiv.org/pdf/2207.08179v1 | 2207.08179 | 10.1016/j.csl.2022.101369 | 16 | 1 | false | null | Computer Speech and Language | 0.3076 |
76dacb0235a6eaf4be22d94b32c4afbf5af19054e49d21cc0d897a6fa28462b7 | [
"arxiv",
"semantic_scholar"
] | AlphaFold predicts the most complex protein knot and composite protein knots | The computer artificial intelligence system AlphaFold has recently predicted previously unknown three-dimensional structures of thousands of proteins. Focusing on the subset with high-confidence scores, we algorithmically analyze these predictions for cases where the protein backbone exhibits rare topological complexit... | [
"Maarten A. Brems",
"Robert Runkel",
"Todd O. Yeates",
"Peter Virnau"
] | [
"q-bio.BM",
"cond-mat.soft",
"physics.bio-ph"
] | [
"Biology",
"Physics",
"Medicine"
] | 2022-07-15T00:00:00 | https://arxiv.org/abs/2207.07410 | https://arxiv.org/pdf/2207.07410v1 | 2207.07410 | 10.1002/pro.4380 | 42 | 1 | false | null | Protein Science | 0.4084 |
1e9420f34df72fc29b6463fbf7b111fd7575d7e8431b3b873f5310596dc46efd | [
"arxiv",
"semantic_scholar"
] | Linguistically inspired roadmap for building biologically reliable protein language models | Deep neural-network-based language models (LMs) are increasingly applied to large-scale protein sequence data to predict protein function. However, being largely black-box models and thus challenging to interpret, current protein LM approaches do not contribute to a fundamental understanding of sequence-function mappin... | [
"Mai Ha Vu",
"Rahmad Akbar",
"Philippe A. Robert",
"Bartlomiej Swiatczak",
"Victor Greiff",
"Geir Kjetil Sandve",
"Dag Trygve Truslew Haug"
] | [
"q-bio.QM",
"cs.LG"
] | [
"Computer Science",
"Biology"
] | 2022-07-03T00:00:00 | https://arxiv.org/abs/2207.00982 | https://arxiv.org/pdf/2207.00982v2 | 2207.00982 | 10.1038/s42256-023-00637-1 | 48 | 0 | false | null | Nature Machine Intelligence | 0.4225 |
055ed6de90a53dd4dc89635e983bb10c1039828119bb9a1527f4e1ae4d3a5c16 | [
"arxiv",
"semantic_scholar"
] | ProGen2: Exploring the Boundaries of Protein Language Models | Attention-based models trained on protein sequences have demonstrated incredible success at classification and generation tasks relevant for artificial intelligence-driven protein design. However, we lack a sufficient understanding of how very large-scale models and data play a role in effective protein model developme... | [
"Erik Nijkamp",
"Jeffrey Ruffolo",
"Eli N. Weinstein",
"Nikhil Naik",
"Ali Madani"
] | [
"cs.LG",
"q-bio.QM"
] | [
"Computer Science",
"Medicine",
"Biology"
] | 2022-06-27T00:00:00 | https://arxiv.org/abs/2206.13517 | https://arxiv.org/pdf/2206.13517v1 | 2206.13517 | 10.48550/arXiv.2206.13517 | 530 | 52 | true | https://github.com/salesforce/progen | Cell Systems | 0.8621 |
b84657d4b8ae6a629d749eeed309fe6cf77b9dcb0042d8da28d6aee22af300fc | [
"arxiv",
"semantic_scholar"
] | PSP: Million-level Protein Sequence Dataset for Protein Structure Prediction | Proteins are essential component of human life and their structures are important for function and mechanism analysis. Recent work has shown the potential of AI-driven methods for protein structure prediction. However, the development of new models is restricted by the lack of dataset and benchmark training procedure. ... | [
"Sirui Liu",
"Jun Zhang",
"Haotian Chu",
"Min Wang",
"Boxin Xue",
"Ningxi Ni",
"Jialiang Yu",
"Yuhao Xie",
"Zhenyu Chen",
"Mengyun Chen",
"Yuan Liu",
"Piya Patra",
"Fan Xu",
"Jie Chen",
"Zidong Wang",
"Lijiang Yang",
"Fan Yu",
"Lei Chen",
"Yi Qin Gao"
] | [
"q-bio.BM",
"cs.LG"
] | [
"Biology",
"Computer Science"
] | 2022-06-24T00:00:00 | https://arxiv.org/abs/2206.12240 | https://arxiv.org/pdf/2206.12240v1 | 2206.12240 | 10.48550/arXiv.2206.12240 | 15 | 1 | true | null | arXiv.org | 0.301 |
d9e27626bba2df65bbc41302170cdfb76829433c486970b3aa145965af4ffacb | [
"arxiv",
"semantic_scholar"
] | Transformer Neural Networks Attending to Both Sequence and Structure for Protein Prediction Tasks | The increasing number of protein sequences decoded from genomes is opening up new avenues of research on linking protein sequence to function with transformer neural networks. Recent research has shown that the number of known protein sequences supports learning useful, task-agnostic sequence representations via transf... | [
"Anowarul Kabir",
"Amarda Shehu"
] | [
"cs.LG",
"cs.AI",
"q-bio.QM"
] | [
"Computer Science",
"Biology"
] | 2022-06-17T00:00:00 | https://arxiv.org/abs/2206.11057 | https://arxiv.org/pdf/2206.11057v1 | 2206.11057 | 10.48550/arXiv.2206.11057 | 5 | 0 | false | null | arXiv.org | 0.1945 |
3bd8f8baac2439a350656586bfb06ef230bacf1705d0e80a15c74988beb289c6 | [
"arxiv",
"semantic_scholar"
] | Exploring evolution-aware & -free protein language models as protein function predictors | Large-scale Protein Language Models (PLMs) have improved performance in protein prediction tasks, ranging from 3D structure prediction to various function predictions. In particular, AlphaFold, a ground-breaking AI system, could potentially reshape structural biology. However, the utility of the PLM module in AlphaFold... | [
"Mingyang Hu",
"Fajie Yuan",
"Kevin K. Yang",
"Fusong Ju",
"Jin Su",
"Hui Wang",
"Fei Yang",
"Qiuyang Ding"
] | [
"q-bio.QM",
"cs.AI"
] | [
"Biology",
"Computer Science"
] | 2022-06-14T00:00:00 | https://arxiv.org/abs/2206.06583 | https://arxiv.org/pdf/2206.06583v2 | 2206.06583 | 10.52202/068431-2817 | 62 | 4 | true | https://github.com/elttaes/Revisiting-PLMs | Neural Information Processing Systems | 0.4498 |
82cc587f4dc12a868f8ca0764538eef20414236f8b86bd0bc041c7473b53acfb | [
"arxiv",
"semantic_scholar"
] | DeepStruct: Pretraining of Language Models for Structure Prediction | We introduce a method for improving the structural understanding abilities of language models. Unlike previous approaches that finetune the models with task-specific augmentation, we pretrain language models on a collection of task-agnostic corpora to generate structures from text. Our structure pretraining enables zer... | [
"Chenguang Wang",
"Xiao Liu",
"Zui Chen",
"Haoyun Hong",
"Jie Tang",
"Dawn Song"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2022-05-21T00:00:00 | https://arxiv.org/abs/2205.10475 | https://arxiv.org/pdf/2205.10475v2 | 2205.10475 | 10.48550/arXiv.2205.10475 | 98 | 14 | false | null | Findings | 0.588 |
ac8c5bc0efc7a95f643f2d8c0a98cb29c345aea56493a49a7d90c5780c27ac4e | [
"arxiv",
"semantic_scholar"
] | MAS2HP: A Multi Agent System to Predict Protein Structure in 2D HP model | Protein Structure Prediction (PSP) is an unsolved problem in the field of computational biology. The problem of protein structure prediction is about predicting the native conformation of a protein, while its sequence of amino acids is known. Regarding processing limitations of current computer systems, all-atom simula... | [
"Hossein Parineh",
"Nasser Mozayani"
] | [
"q-bio.BM",
"cs.AI"
] | [
"Biology",
"Computer Science"
] | 2022-05-11T00:00:00 | https://arxiv.org/abs/2205.08451 | https://arxiv.org/pdf/2205.08451v4 | 2205.08451 | 10.48550/arXiv.2205.08451 | 0 | 0 | false | null | arXiv.org | 0 |
db34d37c5d59e4651841394a2935b510ab7537a24b3591a96c15a77b5c796ea8 | [
"arxiv",
"semantic_scholar"
] | Training Language Models with Language Feedback | Pretrained language models often do not perform tasks in ways that are in line with our preferences, e.g., generating offensive text or factually incorrect summaries. Recent work approaches the above issue by learning from a simple form of human evaluation: comparisons between pairs of model-generated task outputs. Com... | [
"Jérémy Scheurer",
"Jon Ander Campos",
"Jun Shern Chan",
"Angelica Chen",
"Kyunghyun Cho",
"Ethan Perez"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2022-04-29T00:00:00 | https://arxiv.org/abs/2204.14146 | https://arxiv.org/pdf/2204.14146v4 | 2204.14146 | null | 58 | 4 | false | null | null | 0.4427 |
d386a7f6260fb3421362df1754a181ac0f75e63eea99f4c4548dd1620aabb2b3 | [
"arxiv",
"semantic_scholar"
] | Graph neural networks and attention-based CNN-LSTM for protein classification | This paper focuses on three critical problems on protein classification. Firstly, Carbohydrate-active enzyme (CAZyme) classification can help people to understand the properties of enzymes. However, one CAZyme may belong to several classes. This leads to Multi-label CAZyme classification. Secondly, to capture informati... | [
"Zhuangwei Shi",
"Bo Li"
] | [
"q-bio.BM",
"cs.LG"
] | [
"Computer Science",
"Biology"
] | 2022-04-20T00:00:00 | https://arxiv.org/abs/2204.09486 | https://arxiv.org/pdf/2204.09486v2 | 2204.09486 | 10.48550/arXiv.2204.09486 | 8 | 0 | true | https://github.com/zshicode/GNN-AttCL-protein | arXiv.org | 0.2386 |
95fbc6a0fe6094d23f413dc423a6a3fe5dd54fdda0527743bfd2b0d25b37ff02 | [
"arxiv",
"semantic_scholar"
] | Generative power of a protein language model trained on multiple sequence alignments | Computational models starting from large ensembles of evolutionarily related protein sequences capture a representation of protein families and learn constraints associated to protein structure and function. They thus open the possibility for generating novel sequences belonging to protein families. Protein language mo... | [
"Damiano Sgarbossa",
"Umberto Lupo",
"Anne-Florence Bitbol"
] | [
"q-bio.BM",
"cs.LG",
"q-bio.QM"
] | [
"Biology",
"Medicine",
"Computer Science"
] | 2022-04-14T00:00:00 | https://arxiv.org/abs/2204.07110 | https://arxiv.org/pdf/2204.07110v2 | 2204.07110 | 10.7554/eLife.79854 | 44 | 2 | false | null | bioRxiv | 0.4133 |
45a5297b58f3db93814cd1adef0c8c65521003b1185581e274fcfb2f8a12b71a | [
"arxiv",
"semantic_scholar"
] | Inferring Rewards from Language in Context | In classic instruction following, language like "I'd like the JetBlue flight" maps to actions (e.g., selecting that flight). However, language also conveys information about a user's underlying reward function (e.g., a general preference for JetBlue), which can allow a model to carry out desirable actions in new contex... | [
"Jessy Lin",
"Daniel Fried",
"Dan Klein",
"Anca Dragan"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2022-04-05T00:00:00 | https://arxiv.org/abs/2204.02515 | https://arxiv.org/pdf/2204.02515v1 | 2204.02515 | 10.48550/arXiv.2204.02515 | 74 | 4 | true | https://github.com/jlin816/rewards-from-language | Annual Meeting of the Association for Computational Linguistics | 0.4688 |
0ead8b510fc2ad2c40be361999912d3dd6efdb20bbb33d5e3cb11dc49fc2a124 | [
"arxiv",
"semantic_scholar"
] | Protein language models trained on multiple sequence alignments learn phylogenetic relationships | Self-supervised neural language models with attention have recently been applied to biological sequence data, advancing structure, function and mutational effect prediction. Some protein language models, including MSA Transformer and AlphaFold's EvoFormer, take multiple sequence alignments (MSAs) of evolutionarily rela... | [
"Umberto Lupo",
"Damiano Sgarbossa",
"Anne-Florence Bitbol"
] | [
"q-bio.BM",
"cs.LG",
"q-bio.QM"
] | [
"Medicine",
"Biology",
"Computer Science"
] | 2022-03-29T00:00:00 | https://arxiv.org/abs/2203.15465 | https://arxiv.org/pdf/2203.15465v2 | 2203.15465 | 10.1038/s41467-022-34032-y | 61 | 3 | false | null | bioRxiv | 0.4481 |
df53de26879e8e8126b1d017f6db71662b14b46d1510765e615bd12370f994d0 | [
"arxiv",
"semantic_scholar"
] | Protein Representation Learning by Geometric Structure Pretraining | Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein function or structure. Existing approaches usually pretrain protein language models on a large number of unlabeled amino acid sequences and then finetune the models with some labeled data in downstream tas... | [
"Zuobai Zhang",
"Minghao Xu",
"Arian Jamasb",
"Vijil Chenthamarakshan",
"Aurelie Lozano",
"Payel Das",
"Jian Tang"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2022-03-11T00:00:00 | https://arxiv.org/abs/2203.06125 | https://arxiv.org/pdf/2203.06125v5 | 2203.06125 | 10.48550/arXiv.2203.06125 | 322 | 50 | true | https://github.com/DeepGraphLearning/GearNet | International Conference on Learning Representations | 0.8538 |
e934605fdc528234ee6032420a05c41f9205aa8c079f4d8d5ddfb417b3cf7a43 | [
"arxiv",
"semantic_scholar"
] | FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours | Protein structure prediction helps to understand gene translation and protein function, which is of growing interest and importance in structural biology. The AlphaFold model, which used transformer architecture to achieve atomic-level accuracy in protein structure prediction, was a significant breakthrough. However, t... | [
"Shenggan Cheng",
"Xuanlei Zhao",
"Guangyang Lu",
"Jiarui Fang",
"Zhongming Yu",
"Tian Zheng",
"Ruidong Wu",
"Xiwen Zhang",
"Jian Peng",
"Yang You"
] | [
"cs.LG",
"cs.AI",
"cs.DC",
"q-bio.QM"
] | [
"Computer Science",
"Biology"
] | 2022-03-02T00:00:00 | https://arxiv.org/abs/2203.00854 | https://arxiv.org/pdf/2203.00854v3 | 2203.00854 | 10.48550/arXiv.2203.00854 | 42 | 7 | false | null | arXiv.org | 0.4515 |
24c7f0ae69af37c4a0cc5f9d807581a91fc5079415a84673b31c7be5877b8bea | [
"arxiv",
"semantic_scholar"
] | Collective Variable for Metadynamics Derived from AlphaFold Output | AlphaFold is a neural-network-based tool for the prediction of 3D structures of protein. In CASP14, a blind structure prediction challenge, it performed significantly better than other competitors, which makes it the best available structure prediction tool. One of the outputs of AlphaFold is the probability profile of... | [
"Vojtěch Spiwok",
"Martin Kurečka",
"Aleš Křenek"
] | [
"q-bio.BM"
] | [
"Medicine",
"Biology"
] | 2022-02-17T00:00:00 | https://arxiv.org/abs/2203.04848 | https://arxiv.org/pdf/2203.04848v2 | 2203.04848 | 10.3389/fmolb.2022.878133 | 12 | 0 | false | null | Frontiers in Molecular Biosciences | 0.2785 |
9b68f0a48b4de6449e9db2dcff8a084ac39980c916ed2f234f7bd5681af9ab6f | [
"arxiv",
"semantic_scholar"
] | Proteome-scale Deployment of Protein Structure Prediction Workflows on the Summit Supercomputer | Deep learning has contributed to major advances in the prediction of protein structure from sequence, a fundamental problem in structural bioinformatics. With predictions now approaching the accuracy of crystallographic resolution in some cases, and with accelerators like GPUs and TPUs making inference using large mode... | [
"Mu Gao",
"Mark Coletti",
"Russell B. Davidson",
"Ryan Prout",
"Subil Abraham",
"Benjamin Hernandez",
"Ada Sedova"
] | [
"q-bio.QM",
"q-bio.BM"
] | [
"Computer Science",
"Biology"
] | 2022-01-25T00:00:00 | https://arxiv.org/abs/2201.10024 | https://arxiv.org/pdf/2201.10024v1 | 2201.10024 | 10.1109/IPDPSW55747.2022.00045 | 12 | 0 | false | null | IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum | 0.2785 |
e5a194a647ea91432c003704f76ca92fa7fe50a27ed755811cfd146d1a2363fd | [
"arxiv",
"semantic_scholar"
] | AlphaFold Accelerates Artificial Intelligence Powered Drug Discovery: Efficient Discovery of a Novel Cyclin-dependent Kinase 20 (CDK20) Small Molecule Inhibitor | The AlphaFold computer program predicted protein structures for the whole human genome, which has been considered as a remarkable breakthrough both in artificial intelligence (AI) application and structural biology. Despite the varying confidence level, these predicted structures still could significantly contribute to... | [
"Feng Ren",
"Xiao Ding",
"Min Zheng",
"Mikhail Korzinkin",
"Xin Cai",
"Wei Zhu",
"Alexey Mantsyzov",
"Alex Aliper",
"Vladimir Aladinskiy",
"Zhongying Cao",
"Shanshan Kong",
"Xi Long",
"Bonnie Hei Man Liu",
"Yingtao Liu",
"Vladimir Naumov",
"Anastasia Shneyderman",
"Ivan V. Ozerov",
... | [
"q-bio.BM",
"cs.AI",
"cs.LG",
"q-bio.MN"
] | [
"Computer Science",
"Biology"
] | 2022-01-21T00:00:00 | https://arxiv.org/abs/2201.09647 | https://arxiv.org/pdf/2201.09647v2 | 2201.09647 | null | 13 | 0 | false | null | arXiv.org | 0.2865 |
69c67ebf2c5650b810c92e6ce1ba0196c3f615a7ec74e281b9ea022b0d59ea38 | [
"arxiv",
"semantic_scholar"
] | Controllable Protein Design with Language Models | The 21st century is presenting humankind with unprecedented environmental and medical challenges. The ability to design novel proteins tailored for specific purposes could transform our ability to respond timely to these issues. Recent advances in the field of artificial intelligence are now setting the stage to make t... | [
"Noelia Ferruz",
"Birte Höcker"
] | [
"q-bio.BM"
] | [
"Biology",
"Computer Science"
] | 2022-01-18T00:00:00 | https://arxiv.org/abs/2201.07338 | https://arxiv.org/pdf/2201.07338v2 | 2201.07338 | 10.1038/s42256-022-00499-z | 204 | 6 | false | null | Nature Machine Intelligence | 0.5779 |
91de8db4d075ee748f03df8407735ac33125da2b43f8e9dfa7f311502d15e442 | [
"arxiv",
"semantic_scholar"
] | Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents | Can world knowledge learned by large language models (LLMs) be used to act in interactive environments? In this paper, we investigate the possibility of grounding high-level tasks, expressed in natural language (e.g. "make breakfast"), to a chosen set of actionable steps (e.g. "open fridge"). While prior work focused o... | [
"Wenlong Huang",
"Pieter Abbeel",
"Deepak Pathak",
"Igor Mordatch"
] | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CV",
"cs.RO"
] | [
"Computer Science"
] | 2022-01-18T00:00:00 | https://arxiv.org/abs/2201.07207 | https://arxiv.org/pdf/2201.07207v2 | 2201.07207 | null | 1,599 | 98 | false | null | International Conference on Machine Learning | 0.9978 |
339ba59de69abaf894180ddfbf4200e7b66a6c727c5e7f026ec11123a0121e7a | [
"arxiv",
"semantic_scholar"
] | Multimodal Pre-Training Model for Sequence-based Prediction of Protein-Protein Interaction | Protein-protein interactions (PPIs) are essentials for many biological processes where two or more proteins physically bind together to achieve their functions. Modeling PPIs is useful for many biomedical applications, such as vaccine design, antibody therapeutics, and peptide drug discovery. Pre-training a protein mod... | [
"Yang Xue",
"Zijing Liu",
"Xiaomin Fang",
"Fan Wang"
] | [
"q-bio.BM",
"cs.LG"
] | [
"Computer Science",
"Biology"
] | 2021-12-09T00:00:00 | https://arxiv.org/abs/2112.04814 | https://arxiv.org/pdf/2112.04814v1 | 2112.04814 | null | 12 | 1 | false | null | null | 0.2785 |
77fd0435e028f9da4011ab8c32f68c9619ba67ef53d966c04131d2c7c63c071f | [
"arxiv",
"semantic_scholar"
] | ParaFold: Paralleling AlphaFold for Large-Scale Predictions | AlphaFold predicts protein structures from the amino acid sequence at or near experimental resolution, solving the 50-year-old protein folding challenge, leading to progress by transforming large-scale genomics data into protein structures. AlphaFold will also greatly change the scientific research model from low-throu... | [
"Bozitao Zhong",
"Xiaoming Su",
"Minhua Wen",
"Sichen Zuo",
"Liang Hong",
"James Lin"
] | [
"q-bio.BM"
] | [
"Computer Science",
"Biology"
] | 2021-11-11T00:00:00 | https://arxiv.org/abs/2111.06340 | https://arxiv.org/pdf/2111.06340v2 | 2111.06340 | 10.1145/3503470.3503471 | 43 | 4 | true | null | null | 0.4109 |
9cd4b74dcd3244d7653f463cf3ddd1af173fa9c4b5178195de64946b6fd451dc | [
"arxiv",
"semantic_scholar"
] | Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model | Understanding protein sequences is vital and urgent for biology, healthcare, and medicine. Labeling approaches are expensive yet time-consuming, while the amount of unlabeled data is increasing quite faster than that of the labeled data due to low-cost, high-throughput sequencing methods. In order to extract knowledge ... | [
"Liang He",
"Shizhuo Zhang",
"Lijun Wu",
"Huanhuan Xia",
"Fusong Ju",
"He Zhang",
"Siyuan Liu",
"Yingce Xia",
"Jianwei Zhu",
"Pan Deng",
"Bin Shao",
"Tao Qin",
"Tie-Yan Liu"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2021-10-29T00:00:00 | https://arxiv.org/abs/2110.15527 | https://arxiv.org/pdf/2110.15527v1 | 2110.15527 | null | 36 | 4 | false | null | arXiv.org | 0.3921 |
97c4bdba865f46496ebc15b9fda3e20c5cf9ba1f4391acb8420e3eff2cf50e39 | [
"arxiv",
"semantic_scholar"
] | An Open Natural Language Processing Development Framework for EHR-based Clinical Research: A case demonstration using the National COVID Cohort Collaborative (N3C) | While we pay attention to the latest advances in clinical natural language processing (NLP), we can notice some resistance in the clinical and translational research community to adopt NLP models due to limited transparency, interpretability, and usability. In this study, we proposed an open natural language processing... | [
"Sijia Liu",
"Andrew Wen",
"Liwei Wang",
"Huan He",
"Sunyang Fu",
"Robert Miller",
"Andrew Williams",
"Daniel Harris",
"Ramakanth Kavuluru",
"Mei Liu",
"Noor Abu-el-rub",
"Dalton Schutte",
"Rui Zhang",
"Masoud Rouhizadeh",
"John D. Osborne",
"Yongqun He",
"Umit Topaloglu",
"Stephan... | [
"cs.CL",
"cs.IR"
] | [
"Computer Science",
"Medicine"
] | 2021-10-20T00:00:00 | https://arxiv.org/abs/2110.10780 | https://arxiv.org/pdf/2110.10780v3 | 2110.10780 | 10.1093/jamia/ocad134 | 18 | 0 | false | null | null | 0.3197 |
a3ea58588a590709d489bb96a26e3287eb48130fc9366c4b000e6dbfe219bc56 | [
"arxiv",
"semantic_scholar"
] | Application of Sequence Embedding in Protein Sequence-Based Predictions | In sequence-based predictions, conventionally an input sequence is represented by a multiple sequence alignment (MSA) or a representation derived from MSA, such as a position-specific scoring matrix. Recently, inspired by the development in natural language processing, several applications of sequence embedding have be... | [
"Nabil Ibtehaz",
"Daisuke Kihara"
] | [
"q-bio.QM"
] | [
"Biology"
] | 2021-10-14T00:00:00 | https://arxiv.org/abs/2110.07609 | https://arxiv.org/pdf/2110.07609v1 | 2110.07609 | null | 15 | 1 | false | null | null | 0.301 |
0fb351eba72fa98ebf416d3a76608e2e7b4311180fd6ca522d53d590dc5d2888 | [
"arxiv",
"semantic_scholar"
] | Is Attention always needed? A Case Study on Language Identification from Speech | Language Identification (LID) is a crucial preliminary process in the field of Automatic Speech Recognition (ASR) that involves the identification of a spoken language from audio samples. Contemporary systems that can process speech in multiple languages require users to expressly designate one or more languages prior ... | [
"Atanu Mandal",
"Santanu Pal",
"Indranil Dutta",
"Mahidas Bhattacharya",
"Sudip Kumar Naskar"
] | [
"cs.LG",
"cs.CL",
"cs.SD",
"eess.AS",
"eess.SP"
] | [
"Computer Science",
"Engineering"
] | 2021-10-05T00:00:00 | https://arxiv.org/abs/2110.03427 | https://arxiv.org/pdf/2110.03427v3 | 2110.03427 | 10.1017/nlp.2024.22 | 9 | 0 | false | null | Social Science Research Network | 0.25 |
96a19c5b438ef8ec6c9d736475929dde3896228bf9d19bec5f9344b86a13bd01 | [
"arxiv",
"semantic_scholar"
] | Imposing Relation Structure in Language-Model Embeddings Using Contrastive Learning | Though language model text embeddings have revolutionized NLP research, their ability to capture high-level semantic information, such as relations between entities in text, is limited. In this paper, we propose a novel contrastive learning framework that trains sentence embeddings to encode the relations in a graph st... | [
"Christos Theodoropoulos",
"James Henderson",
"Andrei C. Coman",
"Marie-Francine Moens"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2021-09-02T00:00:00 | https://arxiv.org/abs/2109.00840 | https://arxiv.org/pdf/2109.00840v2 | 2109.00840 | 10.18653/v1/2021.conll-1.27 | 18 | 0 | false | null | Conference on Computational Natural Language Learning | 0.3197 |
1b03ddf9afc17cb0f5624526e4ce869ed782a872eea294f651be5a373f23eb48 | [
"arxiv",
"semantic_scholar"
] | Modeling Protein Using Large-scale Pretrain Language Model | Protein is linked to almost every life process. Therefore, analyzing the biological structure and property of protein sequences is critical to the exploration of life, as well as disease detection and drug discovery. Traditional protein analysis methods tend to be labor-intensive and time-consuming. The emergence of de... | [
"Yijia Xiao",
"Jiezhong Qiu",
"Ziang Li",
"Chang-Yu Hsieh",
"Jie Tang"
] | [
"cs.LG",
"cs.CL",
"q-bio.BM"
] | [
"Computer Science",
"Biology"
] | 2021-08-17T00:00:00 | https://arxiv.org/abs/2108.07435 | https://arxiv.org/pdf/2108.07435v2 | 2108.07435 | null | 41 | 2 | true | https://github.com/THUDM/ProteinLM | arXiv.org | 0.4058 |
a55190c481c3d245c9750dca378a84382b9a3b52bf66896b377f790ed41f55a5 | [
"arxiv",
"semantic_scholar"
] | AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models | Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT (Devlin et al., 2019). Few studies have bee... | [
"Yichun Yin",
"Cheng Chen",
"Lifeng Shang",
"Xin Jiang",
"Xiao Chen",
"Qun Liu"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2021-07-29T00:00:00 | https://arxiv.org/abs/2107.13686 | https://arxiv.org/pdf/2107.13686v1 | 2107.13686 | 10.18653/v1/2021.acl-long.400 | 52 | 6 | true | https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/AutoTinyBERT | Annual Meeting of the Association for Computational Linguistics | 0.4311 |
9b6b8c235ec0bba8eb20494e2462915a47d369000b3931a7ffbe690f2b19d184 | [
"arxiv",
"semantic_scholar"
] | gaBERT -- an Irish Language Model | The BERT family of neural language models have become highly popular due to their ability to provide sequences of text with rich context-sensitive token encodings which are able to generalise well to many NLP tasks. We introduce gaBERT, a monolingual BERT model for the Irish language. We compare our gaBERT model to mul... | [
"James Barry",
"Joachim Wagner",
"Lauren Cassidy",
"Alan Cowap",
"Teresa Lynn",
"Abigail Walsh",
"Mícheál J. Ó Meachair",
"Jennifer Foster"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2021-07-27T00:00:00 | https://arxiv.org/abs/2107.12930 | https://arxiv.org/pdf/2107.12930v4 | 2107.12930 | 10.63317/2c485c2jd2yz | 21 | 3 | false | null | International Conference on Language Resources and Evaluation | 0.3356 |
315cd8f83a1bd08ca91fb430280a87acc68c3971b6987e57ae7d7dad9aafb99d | [
"arxiv",
"semantic_scholar"
] | Protein-RNA interaction prediction with deep learning: Structure matters | Protein-RNA interactions are of vital importance to a variety of cellular activities. Both experimental and computational techniques have been developed to study the interactions. Due to the limitation of the previous database, especially the lack of protein structure data, most of the existing computational methods re... | [
"Junkang Wei",
"Siyuan Chen",
"Licheng Zong",
"Xin Gao",
"Yu Li"
] | [
"q-bio.BM",
"cs.LG",
"cs.NE"
] | [
"Computer Science",
"Medicine",
"Biology"
] | 2021-07-26T00:00:00 | https://arxiv.org/abs/2107.12243 | https://arxiv.org/pdf/2107.12243v2 | 2107.12243 | 10.1093/bib/bbab540 | 79 | 4 | false | null | null | 0.4758 |
6b8d57645149a31349de2dffc4c7e38b87246b6c30d7f95530efdef59bd5219f | [
"arxiv",
"semantic_scholar"
] | Picard understanding Darmok: A Dataset and Model for Metaphor-Rich Translation in a Constructed Language | Tamarian, a fictional language introduced in the Star Trek episode Darmok, communicates meaning through utterances of metaphorical references, such as "Darmok and Jalad at Tanagra" instead of "We should work together." This work assembles a Tamarian-English dictionary of utterances from the original episode and several... | [
"Peter Jansen",
"Jordan Boyd-Graber"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2021-07-16T00:00:00 | https://arxiv.org/abs/2107.08146 | https://arxiv.org/pdf/2107.08146v2 | 2107.08146 | 10.18653/v1/2022.flp-1.5 | 0 | 0 | false | null | null | 0 |
ff282fac1d8adb608ecbc9ad87b37039c494723216eacafc288086ab6381f1bf | [
"arxiv",
"semantic_scholar"
] | DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction | How and where proteins interface with one another can ultimately impact the proteins' functions along with a range of other biological processes. As such, precise computational methods for protein interface prediction (PIP) come highly sought after as they could yield significant advances in drug discovery and design a... | [
"Alex Morehead",
"Chen Chen",
"Ada Sedova",
"Jianlin Cheng"
] | [
"q-bio.QM",
"cs.LG",
"q-bio.BM"
] | [
"Medicine",
"Biology",
"Computer Science"
] | 2021-06-06T00:00:00 | https://arxiv.org/abs/2106.04362 | https://arxiv.org/pdf/2106.04362v3 | 2106.04362 | 10.1038/s41597-023-02409-3 | 29 | 2 | false | null | Scientific Data | 0.3693 |
5dbb4b09550a8cdcdc7e34a92d75c4e0c8114da7fd3ad441f18ba7f5627e88ec | [
"arxiv",
"semantic_scholar"
] | Investigating Math Word Problems using Pretrained Multilingual Language Models | In this paper, we revisit math word problems~(MWPs) from the cross-lingual and multilingual perspective. We construct our MWP solvers over pretrained multilingual language models using sequence-to-sequence model with copy mechanism. We compare how the MWP solvers perform in cross-lingual and multilingual scenarios. To ... | [
"Minghuan Tan",
"Lei Wang",
"Lingxiao Jiang",
"Jing Jiang"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2021-05-19T00:00:00 | https://arxiv.org/abs/2105.08928 | https://arxiv.org/pdf/2105.08928v3 | 2105.08928 | 10.18653/v1/2022.mathnlp-1.2 | 36 | 6 | false | null | null | 0.4225 |
9924ae9c2084d90d5563a150c8d59036764e1a07efe60ac93871a8ec1f556325 | [
"arxiv",
"semantic_scholar"
] | Updated Standard Model Prediction for $K \to πν\barν$ and $ε_K$ | The rare $K \to πν\barν$ decay modes and the parameter $ε_K$ that measures CP violation in Kaon mixing are sensitive probes of physics beyond the standard model. In this article we provide the updated standard-model prediction for the rare decay modes in detail, and summarise the status of standard-model prediction of ... | [
"Joachim Brod",
"Martin Gorbahn",
"Emmanuel Stamou"
] | [
"hep-ph",
"hep-ex"
] | [
"Physics"
] | 2021-05-06T00:00:00 | https://arxiv.org/abs/2105.02868 | https://arxiv.org/pdf/2105.02868v1 | 2105.02868 | 10.22323/1.391.0056 | 34 | 0 | false | null | null | 0.386 |
8eb8cbbb603d164f56aaa277f0f28ca4a5c96e6463dcd16b185bd9b85c41ee57 | [
"arxiv",
"semantic_scholar"
] | Markov State Models of protein-protein encounters | This chapter reviews how molecular dynamics simulations, experimental data, and Markov state models can synergize to map-out the mechanism of protein-protein association and dissociation. | [
"Simon Olsson"
] | [
"physics.bio-ph",
"physics.chem-ph",
"physics.comp-ph",
"q-bio.BM"
] | [
"Physics",
"Biology"
] | 2021-05-06T00:00:00 | https://arxiv.org/abs/2105.02767 | https://arxiv.org/pdf/2105.02767v1 | 2105.02767 | null | 4 | 0 | false | null | null | 0.1747 |
bdfd894f4c463bc00bce4c23d6489ac88c02c99a005e613b883f1aab89690e7f | [
"arxiv",
"semantic_scholar"
] | HerBERT: Efficiently Pretrained Transformer-based Language Model for Polish | BERT-based models are currently used for solving nearly all Natural Language Processing (NLP) tasks and most often achieve state-of-the-art results. Therefore, the NLP community conducts extensive research on understanding these models, but above all on designing effective and efficient training procedures. Several abl... | [
"Robert Mroczkowski",
"Piotr Rybak",
"Alina Wróblewska",
"Ireneusz Gawlik"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2021-05-04T00:00:00 | https://arxiv.org/abs/2105.01735 | https://arxiv.org/pdf/2105.01735v1 | 2105.01735 | null | 100 | 6 | false | null | Workshop on Balto-Slavic Natural Language Processing | 0.5011 |
7f3b5d9e7b39b74aa82ccd2dc9cd3c8bf4cf6f03757ea85bea07fadf9aae7a3c | [
"arxiv",
"semantic_scholar"
] | An Automated Multiple-Choice Question Generation Using Natural Language Processing Techniques | Automatic multiple-choice question generation (MCQG) is a useful yet challenging task in Natural Language Processing (NLP). It is the task of automatic generation of correct and relevant questions from textual data. Despite its usefulness, manually creating sizeable, meaningful and relevant questions is a time-consumin... | [
"Chidinma A. Nwafor",
"Ikechukwu E. Onyenwe"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2021-03-26T00:00:00 | https://arxiv.org/abs/2103.14757 | https://arxiv.org/pdf/2103.14757v1 | 2103.14757 | 10.5121/ijnlc.2021.10201 | 34 | 1 | false | null | International Journal on Natural Language Computing | 0.386 |
05752e39d929a66d892585069dc76fd1f3d0fa24ac56b004fc15c8fa5d28f33a | [
"arxiv",
"semantic_scholar"
] | Topical Language Generation using Transformers | Large-scale transformer-based language models (LMs) demonstrate impressive capabilities in open text generation. However, controlling the generated text's properties such as the topic, style, and sentiment is challenging and often requires significant changes to the model architecture or retraining and fine-tuning the ... | [
"Rohola Zandie",
"Mohammad H. Mahoor"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2021-03-11T00:00:00 | https://arxiv.org/abs/2103.06434 | https://arxiv.org/pdf/2103.06434v1 | 2103.06434 | 10.1017/s1351324922000031 | 9 | 0 | false | null | Natural Language Engineering | 0.25 |
b8cc755d285058618cf4d1ebb8b2171f13e1b11486bc451ffd9469e08a167133 | [
"arxiv",
"semantic_scholar"
] | Local sequence-structure relationships in proteins | We seek to understand the interplay between amino acid sequence and local structure in proteins. Are some amino acids unique in their ability to fit harmoniously into certain local structures? What is the role of sequence in sculpting the putative native state folds from myriad possible conformations? In order to addre... | [
"Tatjana Škrbić",
"Amos Maritan",
"Achille Giacometti",
"Jayanth R. Banavar"
] | [
"q-bio.BM",
"cond-mat.soft",
"cond-mat.stat-mech",
"physics.bio-ph"
] | [
"Medicine",
"Biology",
"Physics"
] | 2021-01-27T00:00:00 | https://arxiv.org/abs/2101.11724 | https://arxiv.org/pdf/2101.11724v1 | 2101.11724 | 10.1002/pro.4032 | 8 | 0 | false | null | Protein Science | 0.2386 |
825f0ed195d2e49fedc7adbfcdedb05ca807ee6efff89f8d6a2675ec96b34d69 | [
"arxiv",
"semantic_scholar"
] | The IITM Earth System Model (IITM ESM) | Earth System Models (ESM) are important tools that allow us to understand and quantify the physical, chemical & biological mechanisms governing the rates of change of elements of the Earth System, comprising of the atmosphere, ocean, land, cryosphere and biosphere (terrestrial and marine) and related components. ESMs a... | [
"R. Krishnan",
"P. Swapna",
"Ayantika Dey Choudhury",
"Sandeep Narayansetti",
"A. G. Prajeesh",
"Manmeet Singh",
"Aditi Modi",
"Roxy Mathew",
"Ramesh Vellore",
"J. Jyoti",
"T. P. Sabin",
"J. Sanjay",
"Sandip Ingle"
] | [
"physics.ao-ph"
] | [
"Physics"
] | 2021-01-09T00:00:00 | https://arxiv.org/abs/2101.03410 | https://arxiv.org/pdf/2101.03410v1 | 2101.03410 | null | 11 | 0 | false | null | null | 0.2698 |
cc61b8e181b27b78cb4f982172db2360a71e231d508cd710368c15e5d2c6cbdb | [
"arxiv",
"semantic_scholar"
] | Universal Sentence Representation Learning with Conditional Masked Language Model | This paper presents a novel training method, Conditional Masked Language Modeling (CMLM), to effectively learn sentence representations on large scale unlabeled corpora. CMLM integrates sentence representation learning into MLM training by conditioning on the encoded vectors of adjacent sentences. Our English CMLM mode... | [
"Ziyi Yang",
"Yinfei Yang",
"Daniel Cer",
"Jax Law",
"Eric Darve"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2020-12-28T00:00:00 | https://arxiv.org/abs/2012.14388 | https://arxiv.org/pdf/2012.14388v3 | 2012.14388 | 10.18653/v1/2021.emnlp-main.502 | 64 | 9 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.5 |
e8075105a3a55dca7d7d0708e25908ed15885be60e8ef1b36219ab675338bd3b | [
"arxiv",
"semantic_scholar"
] | Pre-training Protein Language Models with Label-Agnostic Binding Pairs Enhances Performance in Downstream Tasks | Less than 1% of protein sequences are structurally and functionally annotated. Natural Language Processing (NLP) community has recently embraced self-supervised learning as a powerful approach to learn representations from unlabeled text, in large part due to the attention-based context-aware Transformer models. In thi... | [
"Modestas Filipavicius",
"Matteo Manica",
"Joris Cadow",
"Maria Rodriguez Martinez"
] | [
"q-bio.BM",
"cs.CL"
] | [
"Biology",
"Computer Science"
] | 2020-12-05T00:00:00 | https://arxiv.org/abs/2012.03084 | https://arxiv.org/pdf/2012.03084v1 | 2012.03084 | null | 18 | 3 | true | https://github.com/PaccMann/paccmann_proteomics | arXiv.org | 0.3197 |
3628ee50dcdadcb6df3b97d1f02ef663cb03389eccf8cac29b9b636a36987a93 | [
"arxiv",
"semantic_scholar"
] | Profile Prediction: An Alignment-Based Pre-Training Task for Protein Sequence Models | For protein sequence datasets, unlabeled data has greatly outpaced labeled data due to the high cost of wet-lab characterization. Recent deep-learning approaches to protein prediction have shown that pre-training on unlabeled data can yield useful representations for downstream tasks. However, the optimal pre-training ... | [
"Pascal Sturmfels",
"Jesse Vig",
"Ali Madani",
"Nazneen Fatema Rajani"
] | [
"cs.LG",
"q-bio.BM"
] | [
"Computer Science",
"Biology"
] | 2020-12-01T00:00:00 | https://arxiv.org/abs/2012.00195 | https://arxiv.org/pdf/2012.00195v1 | 2012.00195 | null | 27 | 2 | false | null | arXiv.org | 0.3618 |
f64108a1d772a0e0eb9ccd0faf87cf879461ecba66eaedd28140cfb54f8472c5 | [
"arxiv",
"semantic_scholar"
] | PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction | Understanding protein structure-function relationships is a key challenge in computational biology, with applications across the biotechnology and pharmaceutical industries. While it is known that protein structure directly impacts protein function, many functional prediction tasks use only protein sequence. In this wo... | [
"Nicolas Swenson",
"Aditi S. Krishnapriyan",
"Aydin Buluc",
"Dmitriy Morozov",
"Katherine Yelick"
] | [
"q-bio.BM",
"cs.LG",
"math.AT"
] | [
"Computer Science",
"Biology",
"Mathematics"
] | 2020-10-30T00:00:00 | https://arxiv.org/abs/2010.16027 | https://arxiv.org/pdf/2010.16027v1 | 2010.16027 | null | 20 | 1 | false | null | arXiv.org | 0.3306 |
7c4c6536f9335766e1d8c5fa8533219c53bda72c83dc66ca7a4dff2370773510 | [
"arxiv",
"semantic_scholar"
] | Discourse structure interacts with reference but not syntax in neural language models | Language models (LMs) trained on large quantities of text have been claimed to acquire abstract linguistic representations. Our work tests the robustness of these abstractions by focusing on the ability of LMs to learn interactions between different linguistic representations. In particular, we utilized stimuli from ps... | [
"Forrest Davis",
"Marten van Schijndel"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2020-10-10T00:00:00 | https://arxiv.org/abs/2010.04887 | https://arxiv.org/pdf/2010.04887v1 | 2010.04887 | 10.18653/v1/2020.conll-1.32 | 24 | 0 | false | null | Conference on Computational Natural Language Learning | 0.3495 |
6715eb9a92f183ed98070ccc830a72b151925ef17e87dd41cb02ee0fca179ebf | [
"arxiv",
"semantic_scholar"
] | Predictive Modeling of Anatomy with Genetic and Clinical Data | We present a semi-parametric generative model for predicting anatomy of a patient in subsequent scans following a single baseline image. Such predictive modeling promises to facilitate novel analyses in both voxel-level studies and longitudinal biomarker evaluation. We capture anatomical change through a combination of... | [
"Adrian V. Dalca",
"Ramesh Sridharan",
"Mert R. Sabuncu",
"Polina Golland"
] | [
"cs.CV"
] | [
"Computer Science",
"Medicine"
] | 2020-10-09T00:00:00 | https://arxiv.org/abs/2010.04757 | https://arxiv.org/pdf/2010.04757v1 | 2010.04757 | 10.1007/978-3-319-24574-4_62 | 7 | 1 | true | https://github.com/adalca/voxelorb | International Conference on Medical Image Computing and Computer-Assisted Intervention | 0.2258 |
d58b9996833d4e39c3d5231db2003bb7501bf1d7a92fc6c450a3d3a83d31e77d | [
"arxiv",
"semantic_scholar"
] | PS8-Net: A Deep Convolutional Neural Network to Predict the Eight-State Protein Secondary Structure | Protein secondary structure is crucial to creating an information bridge between the primary and tertiary (3D) structures. Precise prediction of eight-state protein secondary structure (PSS) has significantly utilized in the structural and functional analysis of proteins in bioinformatics. Deep learning techniques have... | [
"Md Aminur Rab Ratul",
"Maryam Tavakol Elahi",
"M. Hamed Mozaffari",
"WonSook Lee"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2020-09-22T00:00:00 | https://arxiv.org/abs/2009.10380 | https://arxiv.org/pdf/2009.10380v1 | 2009.10380 | 10.1109/DICTA51227.2020.9363393 | 6 | 1 | false | null | International Conference on Digital Image Computing: Techniques and Applications | 0.2113 |
d1fbbc4fc04cea11c50503e68b3173c1d45d63908664588cb1d635accb6430aa | [
"arxiv",
"semantic_scholar"
] | Unsupervised and Supervised Structure Learning for Protein Contact Prediction | Protein contacts provide key information for the understanding of protein structure and function, and therefore contact prediction from sequences is an important problem. Recent research shows that some correctly predicted long-range contacts could help topology-level structure modeling. Thus, contact prediction and co... | [
"Siqi Sun"
] | [
"q-bio.QM",
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Biology",
"Mathematics"
] | 2020-08-31T00:00:00 | https://arxiv.org/abs/2009.00133 | https://arxiv.org/pdf/2009.00133v1 | 2009.00133 | null | 0 | 0 | false | null | arXiv.org | 0 |
7bf8d231344229cac70d58052cefdadc2ad88cc515e039199a2ac16ee725b5e2 | [
"arxiv",
"semantic_scholar"
] | Language Models as Few-Shot Learner for Task-Oriented Dialogue Systems | Task-oriented dialogue systems use four connected modules, namely, Natural Language Understanding (NLU), a Dialogue State Tracking (DST), Dialogue Policy (DP) and Natural Language Generation (NLG). A research challenge is to learn each module with the least amount of samples (i.e., few-shots) given the high cost relate... | [
"Andrea Madotto",
"Zihan Liu",
"Zhaojiang Lin",
"Pascale Fung"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2020-08-14T00:00:00 | https://arxiv.org/abs/2008.06239 | https://arxiv.org/pdf/2008.06239v2 | 2008.06239 | null | 63 | 2 | true | https://github.com/andreamad8/TASK-ORIENTED-LM-FEWSHOT | arXiv.org | 0.4515 |
cca103fdc170fc9f1add89cf2d01f0afb2875dadceeab507e946201cbf42728c | [
"arxiv",
"semantic_scholar"
] | Deep Learning in Protein Structural Modeling and Design | Deep learning is catalyzing a scientific revolution fueled by big data, accessible toolkits, and powerful computational resources, impacting many fields including protein structural modeling. Protein structural modeling, such as predicting structure from amino acid sequence and evolutionary information, designing prote... | [
"Wenhao Gao",
"Sai Pooja Mahajan",
"Jeremias Sulam",
"Jeffrey J. Gray"
] | [
"q-bio.BM",
"cs.LG"
] | [
"Computer Science",
"Biology",
"Medicine"
] | 2020-07-16T00:00:00 | https://arxiv.org/abs/2007.08383 | https://arxiv.org/pdf/2007.08383v1 | 2007.08383 | 10.1016/j.patter.2020.100142 | 189 | 2 | false | null | Patterns | 0.5697 |
ed887cdf04fa19b0c90dd26e26a9da1cc9e56b235b3d32a07ed93d5eef303433 | [
"arxiv",
"semantic_scholar"
] | Near-complete protein structural modelling of the minimal genome | Protein tertiary structure prediction has improved dramatically in recent years. A considerable fraction of various proteomes can be modelled in the absence of structural templates. We ask whether our DMPfold method can model all the proteins without templates in the JCVI-syn3.0 minimal genome, which contains 438 prote... | [
"Joe G Greener",
"Nikita Desai",
"Shaun M Kandathil",
"David T Jones"
] | [
"q-bio.BM"
] | [
"Biology"
] | 2020-07-13T00:00:00 | https://arxiv.org/abs/2007.06623 | https://arxiv.org/pdf/2007.06623v1 | 2007.06623 | null | 2 | 0 | false | null | null | 0.1193 |
6f1837c9548360f384f8a1f2cc7de6e79fc3d4f32704c0576b4c04d3050d46b5 | [
"arxiv",
"semantic_scholar"
] | Towards the Study of Morphological Processing of the Tangkhul Language | There is no or little work on natural language processing of Tangkhul language. The current work is a humble beginning of morphological processing of this language using an unsupervised approach. We use a small corpus collected from different sources of text books, short stories and articles of other topics. Based on t... | [
"Mirinso Shadang",
"Navanath Saharia",
"Thoudam Doren Singh"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2020-06-29T00:00:00 | https://arxiv.org/abs/2006.16212 | https://arxiv.org/pdf/2006.16212v1 | 2006.16212 | null | 3 | 1 | false | null | arXiv.org | 0.1505 |
368f0a0337d8c85d03657efb131a744874d75349e114b2170b05109174313b14 | [
"arxiv",
"semantic_scholar"
] | Experience Grounds Language | Language understanding research is held back by a failure to relate language to the physical world it describes and to the social interactions it facilitates. Despite the incredible effectiveness of language processing models to tackle tasks after being trained on text alone, successful linguistic communication relies ... | [
"Yonatan Bisk",
"Ari Holtzman",
"Jesse Thomason",
"Jacob Andreas",
"Yoshua Bengio",
"Joyce Chai",
"Mirella Lapata",
"Angeliki Lazaridou",
"Jonathan May",
"Aleksandr Nisnevich",
"Nicolas Pinto",
"Joseph Turian"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2020-04-21T00:00:00 | https://arxiv.org/abs/2004.10151 | https://arxiv.org/pdf/2004.10151v3 | 2004.10151 | 10.18653/v1/2020.emnlp-main.703 | 433 | 15 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.6594 |
38fd8ff90780606899a93d07050a8c5e7a4ef9bbae8ce8f637459e323aed783f | [
"arxiv",
"semantic_scholar"
] | Towards Relevance and Sequence Modeling in Language Recognition | The task of automatic language identification (LID) involving multiple dialects of the same language family in the presence of noise is a challenging problem. In these scenarios, the identity of the language/dialect may be reliably present only in parts of the temporal sequence of the speech signal. The conventional ap... | [
"Bharat Padi",
"Anand Mohan",
"Sriram Ganapathy"
] | [
"eess.AS",
"cs.CL",
"cs.LG",
"cs.SD",
"stat.ML"
] | [
"Computer Science",
"Engineering",
"Mathematics"
] | 2020-04-02T00:00:00 | https://arxiv.org/abs/2004.01221 | https://arxiv.org/pdf/2004.01221v1 | 2004.01221 | 10.1109/TASLP.2020.2983580 | 16 | 3 | true | https://github.com/iiscleap/lre-relevance-weighting | IEEE/ACM Transactions on Audio Speech and Language Processing | 0.3076 |
bb21a6a62e8ce128eb8275b44ecea336763c4361c87b7e602c871f97f0c61565 | [
"arxiv",
"semantic_scholar"
] | ProGen: Language Modeling for Protein Generation | Generative modeling for protein engineering is key to solving fundamental problems in synthetic biology, medicine, and material science. We pose protein engineering as an unsupervised sequence generation problem in order to leverage the exponentially growing set of proteins that lack costly, structural annotations. We ... | [
"Ali Madani",
"Bryan McCann",
"Nikhil Naik",
"Nitish Shirish Keskar",
"Namrata Anand",
"Raphael R. Eguchi",
"Po-Ssu Huang",
"Richard Socher"
] | [
"q-bio.BM",
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Biology",
"Mathematics"
] | 2020-03-08T00:00:00 | https://arxiv.org/abs/2004.03497 | https://arxiv.org/pdf/2004.03497v1 | 2004.03497 | 10.1101/2020.03.07.982272 | 349 | 15 | false | null | bioRxiv | 0.636 |
bd4353b428d6e789df8a33bf51f4b75a875cd7d37575787574fa8182d065d764 | [
"arxiv",
"semantic_scholar"
] | Language-Independent Tokenisation Rivals Language-Specific Tokenisation for Word Similarity Prediction | Language-independent tokenisation (LIT) methods that do not require labelled language resources or lexicons have recently gained popularity because of their applicability in resource-poor languages. Moreover, they compactly represent a language using a fixed size vocabulary and can efficiently handle unseen or rare wor... | [
"Danushka Bollegala",
"Ryuichi Kiryo",
"Kosuke Tsujino",
"Haruki Yukawa"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2020-02-25T00:00:00 | https://arxiv.org/abs/2002.11004 | https://arxiv.org/pdf/2002.11004v1 | 2002.11004 | null | 7 | 0 | false | null | International Conference on Language Resources and Evaluation | 0.2258 |
3b9b419d0adb772577a5aba087f2f60960a182b7d20e09036bacd2c6251b4f81 | [
"arxiv",
"semantic_scholar"
] | A glance into the evolution of template-free protein structure prediction methodologies | Prediction of protein structures using computational approaches has been explored for over two decades, paving a way for more focused research and development of algorithms in comparative modelling, ab intio modelling and structure refinement protocols. A tremendous success has been witnessed in template-based modellin... | [
"Surbhi Dhingra",
"Ramanathan Sowdhamini",
"Frédéric Cadet",
"Bernard Offmann"
] | [
"q-bio.QM",
"q-bio.BM"
] | [
"Biology",
"Computer Science",
"Medicine",
"Mathematics"
] | 2020-02-16T00:00:00 | https://arxiv.org/abs/2002.06616 | https://arxiv.org/pdf/2002.06616v2 | 2002.06616 | 10.1016/j.biochi.2020.04.026 | 29 | 2 | false | null | Biochimie | 0.3693 |
0f2d674638f24c41379064005cf33915d5fbac6d62e29a6131240478b41e8ba5 | [
"arxiv",
"semantic_scholar"
] | Using physical features of protein core packing to distinguish real proteins from decoys | The ability to consistently distinguish real protein structures from computationally generated model decoys is not yet a solved problem. One route to distinguish real protein structures from decoys is to delineate the important physical features that specify a real protein. For example, it has long been appreciated tha... | [
"Alex T. Grigas",
"Zhe Mei",
"John D. Treado",
"Zachary A. Levine",
"Lynne Regan",
"Corey S. O'Hern"
] | [
"q-bio.BM",
"cond-mat.soft"
] | [
"Computer Science",
"Medicine",
"Biology",
"Physics"
] | 2020-01-05T00:00:00 | https://arxiv.org/abs/2001.01161 | https://arxiv.org/pdf/2001.01161v1 | 2001.01161 | 10.1002/pro.3914 | 7 | 0 | false | null | Protein Science | 0.2258 |
916714882cf142176bf06d8f8ff14970f4efe6a518600bab299694364abfd81f | [
"arxiv",
"semantic_scholar"
] | From Quantum Chemistry to Networks in Biology: A Graph Spectral Approach to Protein Structure Analyses | In this perspective article, we present a multidisciplinary approach for characterizing protein structure networks. We first place our approach in its historical context and describe the manner in which it synthesizes concepts from quantum chemistry, biology of polymer conformations, matrix mathematics, and percolation... | [
"Vasundhara Gadiyaram",
"Smitha Vishveshwara",
"Saraswathi Vishveshwara"
] | [
"q-bio.MN",
"cond-mat.stat-mech"
] | [
"Biology",
"Physics",
"Medicine",
"Computer Science"
] | 2019-12-25T00:00:00 | https://arxiv.org/abs/1912.11609 | https://arxiv.org/pdf/1912.11609v1 | 1912.11609 | 10.1021/acs.jcim.9b00002 | 31 | 0 | false | null | Journal of Chemical Information and Modeling | 0.3763 |
643398fd07b02c15250407516b2c6c18c5b3a5b308dc9fba6697ef9c5bfe3115 | [
"arxiv",
"semantic_scholar"
] | Hierarchical Character Embeddings: Learning Phonological and Semantic Representations in Languages of Logographic Origin using Recursive Neural Networks | Logographs (Chinese characters) have recursive structures (i.e. hierarchies of sub-units in logographs) that contain phonological and semantic information, as developmental psychology literature suggests that native speakers leverage on the structures to learn how to read. Exploiting these structures could potentially ... | [
"Minh Nguyen",
"Gia H. Ngo",
"Nancy F. Chen"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2019-12-20T00:00:00 | https://arxiv.org/abs/1912.09913 | https://arxiv.org/pdf/1912.09913v2 | 1912.09913 | 10.1109/TASLP.2019.2955246 | 21 | 0 | false | null | IEEE/ACM Transactions on Audio Speech and Language Processing | 0.3356 |
3f82f523408556b365fff2d31260832c069d2c2afc3171d43c6458c1f991627f | [
"arxiv",
"semantic_scholar"
] | Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations | Proteins are the major building blocks of life, and actuators of almost all chemical and biophysical events in living organisms. Their native structures in turn enable their biological functions which have a fundamental role in drug design. This motivates predicting the structure of a protein from its sequence of amino... | [
"Iddo Drori",
"Darshan Thaker",
"Arjun Srivatsa",
"Daniel Jeong",
"Yueqi Wang",
"Linyong Nan",
"Fan Wu",
"Dimitri Leggas",
"Jinhao Lei",
"Weiyi Lu",
"Weilong Fu",
"Yuan Gao",
"Sashank Karri",
"Anand Kannan",
"Antonio Moretti",
"Mohammed AlQuraishi",
"Chen Keasar",
"Itsik Pe'er"
] | [
"q-bio.BM",
"cs.LG",
"stat.ML"
] | [
"Biology",
"Computer Science",
"Mathematics"
] | 2019-11-09T00:00:00 | https://arxiv.org/abs/1911.05531 | https://arxiv.org/pdf/1911.05531v1 | 1911.05531 | null | 12 | 2 | false | null | arXiv.org | 0.2785 |
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