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Understanding protein structure-function relationships is a key challenge in computational biology, with applications across the biotechnology and pharmaceutical industries.
Three-dimensional alpha shapes
Herbert Edelsbrunner and Ernst P Mücke · 1994
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Topological persistence and simplification
Herbert Edelsbrunner, David Letscher, and Afra Zomorodian · 2000
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Biochemistry, 5th edition
Jeremy M Berg, John L Tymoczko, and Lubert Stryer · 2002
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Announcing the worldwide protein data bank
H.M. Berman, K. Henrick, and H. Nakamura · 2003
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Persistent homology-a survey
Herbert Edelsbrunner and John Harer · 2008
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Sifts: Structure integration with function, taxonomy and sequences resource
Sameer Velankar, José M. Dana, Julius Jacobsen, Glen van Ginkel, Paul J. Gane, Jie Luo, Thomas J. Oldfield, Claire O’Donovan, Maria-Jesus Martin, and Gerard J. Kleywegt · 2012
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Time-split cross-validation as a method for estimating the goodness of prospective prediction
Robert P. Sheridan · 2013
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Spectral networks and locally connected networks on graphs, 2014
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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A topological approach for protein classification
Zixuan Cang, Lin Mu, Kedi Wu, Kristopher Opron, Kelin Xia, and Guo-Wei Wei · 2015
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Weight normalization: A simple reparameterization to accelerate training of deep neural networks, 2016
Tim Salimans and Diederik P. Kingma · 2016
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Semi-supervised classification with graph convolutional networks, 2017
Thomas N. Kipf and Max Welling · 2017
Cited alongside, same era.
Deepsite: protein-binding site predictor using 3d-convolutional neural networks
J. Jiménez, S. Doerr, G. Martínez-Rosell, A.S. Rose, and G. De Fabritiis · 2017
Cited alongside, same era.
Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
Cited alongside, same era.
Persistence images: A stable vector representation of persistent homology
Henry Adams, Tegan Emerson, Michael Kirby, Rachel Neville, Chris Peterson, Patrick Shipman, Sofya Chepushtanova, Eric Hanson, Francis Motta, and Lori Ziegelmeier · 2017
Cited alongside, same era.
Adam: A method for stochastic optimization, 2017
The cafa challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens
Naihui Zhou, Yuxiang Jiang, Timothy R Bergquist, Alexandra J Lee, Balint Z Kacsoh, Alex W Crocker, Kimberley A Lewis, George Georghiou, Huy N Nguyen, Md Nafiz Hamid, et al · 2019
Later among the works it cites.
Netgo: improving large-scale protein function prediction with massive network information
Ronghui You, Shuwei Yao, Yi Xiong, Xiaodi Huang, Fengzhu Sun, Hiroshi Mamitsuka, and Shanfeng Zhu · 2019
Later among the works it cites.
Learning representations of persistence barcodes
Christoph D Hofer, Roland Kwitt, and Marc Niethammer · 2019
Later among the works it cites.
Structure-based protein function prediction using graph convolutional networks
Vladimir Gligorijevic, P. Douglas Renfrew, Tomasz Kosciolek, Julia Koehler Leman, Daniel Berenberg, Tommi Vatanen, Chris Chandler, Bryn C. Taylor, Ian M. Fisk, Hera Vlamakis, Ramnik J. Xavier, Rob Knight, Kyunghyun Cho, and Richard Bonneau · 2020
Closest in time.
Perslay: A neural network layer for persistence diagrams and new graph topological signatures
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Diederik P. Kingma and Jimmy Ba · 2017
Cited alongside, same era.
Enzynet: enzyme classification using 3d convolutional neural networks on spatial representation
Afshine Amidi, Shervine Amidi, Dimitrios Vlachakis, Vasileios Megalooikonomou, Nikos Paragios, and Evangelia I Zacharaki · 2018
Cited alongside, same era.
Protein classification with improved topological data analysis
Tamal K Dey and Sayan Mandal · 2018
Cited alongside, same era.
Sifts: updated structure integration with function, taxonomy and sequences resource allows 40-fold increase in coverage of structure-based annotations for proteins
Jose M Dana, Aleksandras Gutmanas, Nidhi Tyagi, Guoying Qi, Claire O’Donovan, Maria Martin, and Sameer Velankar · 2018
Cited alongside, same era.
Uniprot: a worldwide hub of protein knowledge
UniProt Consortium · 2019
Cited alongside, same era.
Mathieu Carrière, Frédéric Chazal, Yuichi Ike, Théo Lacombe, Martin Royer, and Yuhei Umeda · 2020
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Uncovering the topology of time-varying fmri data using cubical persistence
Bastian Rieck, Tristan Yates, Christian Bock, Karsten Borgwardt, Guy Wolf, Nicholas Turk-Browne, and Smita Krishnaswamy · 2020
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Topological descriptors help predict guest adsorption in nanoporous materials
Aditi S Krishnapriyan, Maciej Haranczyk, and Dmitriy Morozov · 2020
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Persistent homology advances interpretable machine learning for nanoporous materials
Aditi S Krishnapriyan, Joseph Montoya, Jens Hummelshøj, and Dmitriy Morozov · 2020
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Classifying protein structures into folds by convolutional neural networks, distance maps, and persistent homology
Yechan Hong, Yongyu Deng, Haofan Cui, Jan Segert, and Jianlin Cheng · 2020
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Persistence enhanced graph neural network
Qi Zhao, Ze Ye, Chao Chen, and Yusu Wang · 2020
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