Neural Potts Model
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Yann LeCun | Zeming Lin | Brandon Amos | Alexander Rives | J. Meier | Tom Sercu | Robert Verkuil | Caroline Chen | Jason Liu
[1] Myle Ott,et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences , 2019, Proceedings of the National Academy of Sciences.
[2] Jinbo Xu,et al. Improved protein structure prediction by deep learning irrespective of co-evolution information , 2020, Nature Machine Intelligence.
[3] Lav R. Varshney,et al. BERTology Meets Biology: Interpreting Attention in Protein Language Models , 2020, bioRxiv.
[4] Burkhard Rost,et al. Modeling aspects of the language of life through transfer-learning protein sequences , 2019, BMC Bioinformatics.
[5] Jianyi Yang,et al. Improved protein structure prediction using predicted interresidue orientations , 2019, Proceedings of the National Academy of Sciences.
[6] John Canny,et al. Evaluating Protein Transfer Learning with TAPE , 2019, bioRxiv.
[7] George M. Church,et al. Unified rational protein engineering with sequence-only deep representation learning , 2019, bioRxiv.
[8] Milot Mirdita,et al. HH-suite3 for fast remote homology detection and deep protein annotation , 2019, BMC Bioinformatics.
[9] Bonnie Berger,et al. Learning protein sequence embeddings using information from structure , 2019, ICLR.
[10] Jinbo Xu. Distance-based protein folding powered by deep learning , 2018, Proceedings of the National Academy of Sciences.
[11] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[12] Alexander M. Rush,et al. Semi-Amortized Variational Autoencoders , 2018, ICML.
[13] David Duvenaud,et al. Inference Suboptimality in Variational Autoencoders , 2018, ICML.
[14] Johannes Söding,et al. Clustering huge protein sequence sets in linear time , 2017, Nature Communications.
[15] Pieter Abbeel,et al. A Simple Neural Attentive Meta-Learner , 2017, ICLR.
[16] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[17] Sergey Levine,et al. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.
[18] Steven L. Brunton,et al. Machine Learning Control – Taming Nonlinear Dynamics and Turbulence , 2016, Fluid Mechanics and Its Applications.
[19] Mark W. Schmidt,et al. Fast Patch-based Style Transfer of Arbitrary Style , 2016, ArXiv.
[20] Luca Bertinetto,et al. Learning feed-forward one-shot learners , 2016, NIPS.
[21] Marcin Andrychowicz,et al. Learning to learn by gradient descent by gradient descent , 2016, NIPS.
[22] A. Tramontano,et al. Critical assessment of methods of protein structure prediction: Progress and new directions in round XI , 2016, Proteins.
[23] Robert D. Finn,et al. The Pfam protein families database: towards a more sustainable future , 2015, Nucleic Acids Res..
[24] David T. Jones,et al. Opportunities and limitations in applying coevolution-derived contacts to protein structure prediction , 2014, Bio Algorithms Med Syst..
[25] Markus Gruber,et al. CCMpred—fast and precise prediction of protein residue–residue contacts from correlated mutations , 2014, Bioinform..
[26] Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.
[27] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[28] D. Baker,et al. Assessing the utility of coevolution-based residue–residue contact predictions in a sequence- and structure-rich era , 2013, Proceedings of the National Academy of Sciences.
[29] E. Aurell,et al. Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.
[30] Massimiliano Pontil,et al. PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments , 2012, Bioinform..
[31] C. Sander,et al. Direct-coupling analysis of residue coevolution captures native contacts across many protein families , 2011, Proceedings of the National Academy of Sciences.
[32] Sivaraman Balakrishnan,et al. Learning generative models for protein fold families , 2011, Proteins.
[33] Yann LeCun,et al. Learning Fast Approximations of Sparse Coding , 2010, ICML.
[34] T. Hwa,et al. Identification of direct residue contacts in protein–protein interaction by message passing , 2009, Proceedings of the National Academy of Sciences.
[35] C. Bailey-Kellogg,et al. Graphical Models of Residue Coupling in Protein Families , 2008, TCBB.
[36] Gregory B. Gloor,et al. Mutual information without the influence of phylogeny or entropy dramatically improves residue contact prediction , 2008, Bioinform..
[37] Chris Bailey-Kellogg,et al. Graphical Models of Residue Coupling in Protein Families , 2005, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[38] Siddhartha S. Srinivasa,et al. Imitation learning for locomotion and manipulation , 2007, 2007 7th IEEE-RAS International Conference on Humanoid Robots.
[39] Peter B. McGarvey,et al. UniRef: comprehensive and non-redundant UniProt reference clusters , 2007, Bioinform..
[40] Jürgen Schmidhuber,et al. Evolving Modular Fast-Weight Networks for Control , 2005, ICANN.
[41] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[42] G. Stormo,et al. Correlated mutations in models of protein sequences: phylogenetic and structural effects , 1999 .
[43] Claude Sammut,et al. A Framework for Behavioural Cloning , 1995, Machine Intelligence 15.
[44] C. Sander,et al. Correlated mutations and residue contacts in proteins , 1994, Proteins.