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Much scientific enquiry across disciplines is founded upon a mechanistic treatment of dynamic systems that ties form to function.
Recovery of protein structure from contact maps
M. Vendruscolo, E. Kussell, and E. Domany. 1997 · 1997
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Protein structure prediction by global optimization of a potential energy function
A. Liwo, J. Lee, D. R. Ripoll, J. Pillardy, and H. A. Scheraga. 1999 · 1999
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Prediction of contact maps with neural networks and correlated mutations
Piero Fariselli, Osvaldo Olmea, Alfonso Valencia, and Rita Casadio. 2001 · 2001
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Prediction of contact maps by GIOHMMs and recurrent neural networks using lateral propagation from all four cardinal corners
Gianluca Pollastri and Pierre Baldi. 2002 · 2002
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Protein contact prediction using patterns of correlation
Nicholas Hamilton, Kevin Burrage, Mark A Ragan, and Thomas Huber. 2004 · 2004
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Improved residue contact prediction using support vector machines and a large feature set
Jianlin Cheng and Pierre Baldi. 2007 · 2007
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How do proteins interact?
D. D. Boehr and P. E. Wright. 2008 · 2008
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A comprehensive assessment of sequence-based and template-based methods for protein contact prediction
Sitao Wu and Yang Zhang. 2008 · 2008
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NNcon: improved protein contact map prediction using 2D-recursive neural networks
Allison N Tegge, Zheng Wang, Jesse Eickholt, and Jianlin Cheng. 2009 · 2009
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Ab initio and template-based prediction of multi-class distance maps by two-dimensional recursive neural networks
Ian Walsh, Davide Baù, Alberto JM Martin, Catherine Mooney, Alessandro Vullo, and Gianluca Pollastri. 2009 · 2009
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ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules
A. Leaver-Fay, M. Tyka, S. M. Lewis, O. F. Lange, J. Thompson, R. Jacak, et al · 2011
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Predicting residue–residue contacts using random forest models
Yunqi Li, Yaping Fang, and Jianwen Fang. 2011 · 2011
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Deep architectures for protein contact map prediction
Pietro Di Lena, Ken Nagata, and Pierre Baldi. 2012 · 2012
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Predicting protein residue–residue contacts using deep networks and boosting
Jesse Eickholt and Jianlin Cheng. 2012 · 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling. 2013 · 2013
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Probabilistic Search and Optimization for Protein Energy Landscapes
A. Shehu. 2013 · 2013
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Toward an accurate prediction of inter-residue distances in proteins using 2d recursive neural networks
P. Kukic, P. Mirabello, G. Tradigo, I. Walsh, P. Veltri, and G. Pollastri. 2014 · 2014
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CONFOLD: residue-residue contact-guided ab initio protein folding
Badri Adhikari, Debswapna Bhattacharya, Renzhi Cao, and Jianlin Cheng. 2015 · 2015
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Deep variational information bottleneck
Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy. 2016 · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016a · 2016
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Variational graph auto-encoders
Thomas N Kipf and Max Welling. 2016b · 2016
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A generative model for protein contact networks
Lorenzo Livi, Enrico Maiorino, Alessandro Giuliani, Antonello Rizzi, and Alireza Sadeghian. 2016 · 2016
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Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics
T. Maximova, R. Moffatt, B. Ma, R. Nussinov, and A. Shehu. 2016 · 2016
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beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner. 2017 · 2017
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Hyunjik Kim and Andriy Mnih. 2018 · 2018
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Enhancing evolutionary couplings with deep convolutional neural networks
Y. Liu, P. Palmedo, Ye Q., B. Berger, and J. Peng. 2018 · 2018
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Information constraints on auto-encoding variational bayes. In Advances in Neural Information Processing Systems . 6114–6125
Romain Lopez, Jeffrey Regier, Michael I Jordan, and Nir Yosef. 2018 · 2018
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Graphvae: Towards generation of small graphs using variational autoencoders. In International Conference on Artificial Neural Networks . Springer, 412–422
Martin Simonovsky and Nikos Komodakis. 2018 · 2018
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Neural-symbolic vqa: Disentangling reasoning from vision and language understanding. In Advances in Neural Information Processing Systems . 1031–1042
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Assessment of model accuracy estimations in CASP12
A. Kryshtafovych, B. Monastyrskyy, K. Fidelis, T. Schwede, and A. Tramontano. 2017 · 2017
Cited alongside, same era.
Variational inference of disentangled latent concepts from unlabeled observations
Abhishek Kumar, Prasanna Sattigeri, and Avinash Balakrishnan. 2017 · 2017
Cited alongside, same era.
Ab initio protein structure prediction
J. Lee, P. Freddolino, and Y. Zhang. 2017 · 2017
Cited alongside, same era.
Accurate de novo prediction of protein contact map by ultra-deep learning model
S. Wang, S. Sun, Z. Li, R. Zhang, and J. Xu. 2017 · 2017
Cited alongside, same era.
Infovae: Information maximizing variational autoencoders
Shengjia Zhao, Jiaming Song, and Stefano Ermon. 2017 · 2017
Cited alongside, same era.
DNCON2: improved protein contact prediction using two-level deep convolutional neural networks
B. Adhikari, J. Hou, and J. Cheng. 2018 · 2018
Cited alongside, same era.
Netgan: Generating graphs via random walks
Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, and Stephan Günnemann. 2018 · 2018
Cited alongside, same era.
Kexin Yi, Jiajun Wu, Chuang Gan, Antonio Torralba, Pushmeet Kohli, and Josh Tenenbaum. 2018 · 2018
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Graphrnn: Generating realistic graphs with deep auto-regressive models
Jiaxuan You, Rex Ying, Xiang Ren, William L Hamilton, and Jure Leskovec. 2018 · 2018
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Secondary Structure and Contact Guided Differential Evolution for Protein Structure Prediction
G. Zhang, L. Ma, X. Wang, and X. Zhou. 2018 · 2018
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Fully differentiable full-atom protein backbone generation
Namrata Anand, Raphael Eguchi, and Po-Ssu Huang. 2019 · 2019
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Learning protein structure with a differentiable simulator. In International Conference on Learning Representations
John Ingraham, Adam Riesselman, Chris Sander, and Debora Marks. 2019 · 2019
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Ensembling multiple raw coevolutionary features with deep residual neural networks for contact-map prediction in CASP13
Y. Li, C. Zhang, E. W. Bell, D.-J. Yu, and Y. Zhang. 2019 · 2019
Later among the works it cites.
Deep generative model driven protein folding simulation
Heng Ma, Debsindhu Bhowmik, Hyungro Lee, Matteo Turilli, Michael T Young, Shantenu Jha, and Arvind Ramanathan. 2019 · 2019
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PconsC4: fast, accurate and hassle-free contact predictions
M. Michel, D. M. Hurtado, and A. Elofsson. 2019 · 2019
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RamaNet: Computational De Novo Protein Design using a Long Short-Term Memory Generative Adversarial Neural Network
Sari Sabban and Mikhail Markovsky. 2019 · 2019
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Nevae: A deep generative model for molecular graphs. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 33. 1110–1117
Bidisha Samanta, DE Abir, Gourhari Jana, Pratim Kumar Chattaraj, Niloy Ganguly, and Manuel Gomez Rodriguez. 2019 · 2019
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Protein structure prediction using multiple deep neural networks in CASP13
A. W. Senior, R. Evans, J. Jumper, J. Kirkpatrick, L. Sifre, et al · 2019
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Using Sequence-Predicted Contacts to Guide Template-free Protein Structure Prediction. In ACM Conf on Bioinf and Comp Biol (BCB) . Niagara Falls, NY, 154–160
A. Zaman, P. Parthasarathy, and A. Shehu. 2019 · 2019
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Balancing multiple objectives in conformation sampling to control decoy diversity in template-free protein structure prediction
A. Zaman and A. Shehu. 2019 · 2019
Later among the works it cites.
Deep learning methods in protein structure prediction
M. Torrisi, G. Pollastri, and Q. Le. 2020 · 2020
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