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Inferring the structural properties of a protein from its amino acid sequence is a challenging yet important problem in biology.
A general method applicable to the search for similarities in the amino acid sequence of two proteins
S B Needleman and C D Wunsch · 1970
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Basic local alignment search tool
S F Altschul, W Gish, W Miller, E W Myers, and D J Lipman · 1990
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SCOP: a structural classification of proteins database for the investigation of sequences and structures
A G Murzin, S E Brenner, T Hubbard, and C Chothia · 1995
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Mapping the protein universe
L Holm and C Sander · 1996
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Understanding protein structure: using scop for fold interpretation
S E Brenner, C Chothia, T J Hubbard, and A G Murzin · 1996
Earlier work this paper cites.
SCOP: a structural classification of proteins database
T J Hubbard, A G Murzin, S E Brenner, and C Chothia · 1997
Earlier work this paper cites.
The protein data bank
Helen M. Berman, John Westbrook, Zukang Feng, Gary Gilliland, T. N. Bhat, Helge Weissig, Ilya N. Shindyalov, and Philip E. Bourne · 2000
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Analysis of protein sequence/structure similarity relationships
Hin Hark Gan, Rebecca A Perlow, Sharmili Roy, Joy Ko, Min Wu, Jing Huang, Shixiang Yan, Angelo Nicoletta, Jonathan Vafai, Ding Sun, Lihua Wang, Joyce E Noah, Samuela Pasquali, and Tamar Schlick · 2002
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TM-align: a protein structure alignment algorithm based on the TM-score
Y. Zhang and J. Skolnick · 2005
Earlier work this paper cites.
The HHpred interactive server for protein homology detection and structure prediction
J. Soding, A. Biegert, and A. N. Lupas · 2005
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Detecting remote evolutionary relationships among proteins by large-scale semantic embedding
Iain Melvin, Jason Weston, William Stafford Noble, and Christina Leslie · 2011
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HMMER web server: interactive sequence similarity searching
R. D. Finn, J. Clements, and S. R. Eddy · 2011
Earlier work this paper cites.
A simple, fast, and effective reparameterization of ibm model 2
Chris Dyer, Victor Chahuneau, and Noah A Smith · 2013
Earlier work this paper cites.
SCOPe: Structural classification of proteins–extended, integrating SCOP and ASTRAL data and classification of new structures
Naomi K Fox, Steven E Brenner, and John-Marc Chandonia · 2014
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Distributed representations of sentences and documents
Quoc Le and Tomas Mikolov · 2014
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Learning bilingual word representations by marginalizing alignments
Tomáš Kočiský, Karl Moritz Hermann, and Phil Blunsom · 2014
Cited alongside, same era.
Pfam: the protein families database
R. D. Finn, A. Bateman, J. Clements, P. Coggill, R. Y. Eberhardt, S. R. Eddy, A. Heger, K. Hetherington, L. Holm, J. Mistry, E. L. Sonnhammer, J. Tate, and M. Punta · 2014
Cited alongside, same era.
Metapsicov: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins
David T Jones, Tanya Singh, Tomasz Kosciolek, and Stuart Tetchner · 2014
Cited alongside, same era.
Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information
Sergey Ovchinnikov, Hetunandan Kamisetty, and David Baker · 2014
Cited alongside, same era.
Continuous distributed representation of biological sequences for deep proteomics and genomics
Ehsaneddin Asgari and Mohammad R. K. Mofrad · 2015
Cited alongside, same era.
Text understanding with the attention sum reader network
Rudolf Kadlec, Martin Schmid, Ondrej Bajgar, and Jan Kleindienst · 2016
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Pairwise word interaction modeling with deep neural networks for semantic similarity measurement
Hua He and Jimmy Lin · 2016
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A decomposable attention model for natural language inference
Ankur P Parikh, Oscar Täckström, Dipanjan Das, and Jakob Uszkoreit · 2016
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A simple but tough-to-beat baseline for sentence embeddings
Sanjeev Arora, Yingyu Liang, and Tengyu Ma · 2017
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Semi-supervised sequence tagging with bidirectional language models
Matthew Peters, Waleed Ammar, Chandra Bhagavatula, and Russell Power · 2017
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Skip-Thought vectors
Ryan Kiros, Yukun Zhu, Ruslan R Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba, and Sanja Fidler · 2015
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ProFET: Feature engineering captures high-level protein functions
D. Ofer and M. Linial · 2015
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A large annotated corpus for learning natural language inference
Samuel R Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning · 2015
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BilBOWA: Fast bilingual distributed representations without word alignments
Stephan Gouws, Yoshua Bengio, and Greg Corrado · 2015
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom · 2015
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Improved semantic representations from Tree-Structured long Short-Term memory networks
Kai Sheng Tai, Richard Socher, and Christopher D Manning · 2015
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Supervised learning of universal sentence representations from natural language inference data
Alexis Conneau, Douwe Kiela, Holger Schwenk, Loic Barrault, and Antoine Bordes · 2017
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Attention-over-attention neural networks for reading comprehension
Yiming Cui, Zhipeng Chen, Si Wei, Shijin Wang, Ting Liu, and Guoping Hu · 2017
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Bidirectional attention flow for machine comprehension
Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi · 2017
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SCOPe: Manual curation and artifact removal in the structural classification of proteins - extended database
John-Marc Chandonia, Naomi K Fox, and Steven E Brenner · 2017
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Accurate de novo prediction of protein contact map by ultra-deep learning model
Sheng Wang, Siqi Sun, Zhen Li, Renyu Zhang, and Jinbo Xu · 2017
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Learned protein embeddings for machine learning
K. K. Yang, Z. Wu, C. N. Bedbrook, and F. H. Arnold · 2018
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Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
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Assessment of contact predictions in casp12: Co-evolution and deep learning coming of age
Joerg Schaarschmidt, Bohdan Monastyrskyy, Andriy Kryshtafovych, and Alexandre MJJ Bonvin · 2018
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Enhancing evolutionary couplings with deep convolutional neural networks
Yang Liu, Perry Palmedo, Qing Ye, Bonnie Berger, and Jian Peng · 2018
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