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For protein sequence datasets, unlabeled data has greatly outpaced labeled data due to the high cost of wet-lab characterization.
Multiple sequence alignment with hierarchical clustering
Florence Corpet. 1988 · 1988
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Clustal v: improved software for multiple sequence alignment
Desmond G Higgins, Alan J Bleasby, and Rainer Fuchs. 1992 · 1992
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Profile hidden markov models
Sean R. Eddy. 1998 · 1998
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Evaluation and improvement of multiple sequence methods for protein secondary structure prediction
James A Cuff and Geoffrey J Barton. 1999 · 1999
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Protein secondary structure prediction based on position-specific scoring matrices
David T Jones. 1999 · 1999
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Application of multiple sequence alignment profiles to improve protein secondary structure prediction
James A Cuff and Geoffrey J Barton. 2000 · 2000
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Protein secondary structure prediction continues to rise
Burkhard Rost. 2001 · 2001
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The cost and value of three-dimensional protein structure
Raymond C Stevens. 2003 · 2003
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Progen: Language modeling for protein generation
Ali Madani, Bryan McCann, Nikhil Naik, Nitish Shirish Keskar, Namrata Anand, Raphael R Eguchi, Po-Ssu Huang, and Richard Socher. 2020 · 2004
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Multiple sequence alignment
Robert C Edgar and Serafim Batzoglou. 2006 · 2006
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Ahmed Elnaggar, Michael Heinzinger, Christian Dallago, Ghalia Rihawi, Yu Wang, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Debsindhu Bhowmik, et al. 2020 · 2007
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Hmmer web server: interactive sequence similarity searching
Robert D Finn, Jody Clements, and Sean R Eddy. 2011 · 2011
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Fast, scalable generation of high-quality protein multiple sequence alignments using clustal omega
Fabian Sievers, Andreas Wilm, David Dineen, Toby J Gibson, Kevin Karplus, Weizhong Li, Rodrigo Lopez, Hamish McWilliam, Michael Remmert, Johannes Söding, et al. 2011 · 2011
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Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning
Jianzhu Ma, Sheng Wang, Zhiyong Wang, and Jinbo Xu. 2015 · 2015
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Local fitness landscape of the green fluorescent protein
Karen S Sarkisyan, Dmitry A Bolotin, Margarita V Meer, Dinara R Usmanova, Alexander S Mishin, George V Sharonov, Dmitry N Ivankov, Nina G Bozhanova, Mikhail S Baranov, Onuralp Soylemez, et al. 2016 · 2016
Proteinnet: a standardized data set for machine learning of protein structure
Mohammed AlQuraishi. 2019 · 2019
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Uniprot: a worldwide hub of protein knowledge
UniProt Consortium. 2019 · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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The pfam protein families database in 2019
Sara El-Gebali, Jaina Mistry, Alex Bateman, Sean R Eddy, Aurélien Luciani, Simon C Potter, Matloob Qureshi, Lorna J Richardson, Gustavo A Salazar, Alfredo Smart, et al. 2019 · 2019
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Netsurfp-2.0: Improved prediction of protein structural features by integrated deep learning
Michael Schantz Klausen, Martin Closter Jespersen, Henrik Nielsen, Kamilla Kjærgaard Jensen, Vanessa Isabell Jurtz, Casper Kaae Sønderby, Morten Otto Alexander Sommer, Ole Winther, Morten Nielsen, Bent Petersen, et al. 2019 · 2019
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Cited alongside, same era.
Global analysis of protein folding using massively parallel design, synthesis, and testing
Gabriel J Rocklin, Tamuka M Chidyausiku, Inna Goreshnik, Alex Ford, Scott Houliston, Alexander Lemak, Lauren Carter, Rashmi Ravichandran, Vikram K Mulligan, Aaron Chevalier, et al. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Learning protein sequence embeddings using information from structure
Tristan Bepler and Bonnie Berger. 2018 · 2018
Cited alongside, same era.
Deepsf: deep convolutional neural network for mapping protein sequences to folds
Jie Hou, Badri Adhikari, and Jianlin Cheng. 2018 · 2018
Cited alongside, same era.
Unified rational protein engineering with sequence-only deep representation learning
Ethan C Alley, Grigory Khimulya, Surojit Biswas, Mohammed AlQuraishi, and George M Church. 2019 · 2019
Cited alongside, same era.
Evaluating protein transfer learning with tape
Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Peter Chen, John Canny, Pieter Abbeel, and Yun Song. 2019 · 2019
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Alexander Rives, Siddharth Goyal, Joshua Meier, Demi Guo, Myle Ott, C Lawrence Zitnick, Jerry Ma, and Rob Fergus. 2019 · 2019
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Self-supervised contrastive learning of protein representations by mutual information maximization
Amy X. Lu, Haoran Zhang, Marzyeh Ghassemi, and Alan Moses. 2020 · 2020
Closest in time.
Transforming the language of life: Transformer neural networks for protein prediction tasks
Ananthan Nambiar, Maeve Elizabeth Heflin, Simon Liu, Sergei Maslov, Mark Hopkins, and Anna Ritz. 2020 · 2020
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Udsmprot: universal deep sequence models for protein classification
Nils Strodthoff, Patrick Wagner, Markus Wenzel, and Wojciech Samek. 2020 · 2020
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