Fetching the paper…
Reading the bibliography…
Proteins perform a large variety of functions in living organisms, thus playing a key role in biology.
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 · 1955
Earlier work this paper cites.
Extrinsic geometry of convex surfaces , volume 35
A. V. Pogorelov · 1973
Earlier work this paper cites.
Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features
Wolfgang Kabsch and Christian Sander · 1983
Earlier work this paper cites.
Basic local alignment search tool
S.F. Altschul, W. Gish, W. Miller, E.W. Myers, and D.J Lipman · 1990
Earlier work this paper cites.
Enzyme Nomenclature 1992
Edwin C. Webb · 1992
Earlier work this paper cites.
Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and bayesian scoring functions
K. T. Simons, C. Kooperberg, E. Huang, and D. Baker · 1997
Earlier work this paper cites.
The Protein Data Bank
H. M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat, H. Weissig, I. N. Shindyalov, and P. E. Bourne · 2000
Earlier work this paper cites.
Functional evolution of two subtly different (similar) folds
Vishal Agrawal and Radha KV Kishan · 2001
Earlier work this paper cites.
Distinguishing enzyme structures from non-enzymes without alignments
Paul D Dobson and Andrew J Doig · 2003
Earlier work this paper cites.
Learning metrics for persistence-based summaries and applications for graph classification
Qi Zhao and Yusu Wang · 2003
Earlier work this paper cites.
Protein length in eukaryotic and prokaryotic proteomes
Luciano Brocchieri and Samuel Karlin · 2005
Earlier work this paper cites.
A tutorial on spectral clustering
Ulrike von Luxburg · 2007
Earlier work this paper cites.
Visualizing high-dimensional data using t-sne
L. van der Maaten and G. E. Hinton · 2008
Earlier work this paper cites.
A minimal sequence code for switching protein structure and function
Patrick A. Alexander, Yanan He, Yihong Chen, John Orban, and Philip N. Bryan · 2009
Earlier work this paper cites.
Transferable coarse grain nonbonded interaction model for amino acids
R. DeVane, W. Shinoda, P. B. Moore, and M. L. Klein · 2009
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E Hinton · 2012
Earlier work this paper cites.
Primo: A transferable coarse-grained force field for proteins
P. Kar, S. M. Gopal, Y.-M. Cheng, A. Predeus, and M. Feig · 2013
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
Earlier work this paper cites.
Continuous distributed representation of biological sequences for deep proteomics and genomics
E. Asgari and M.R.K. Mofrad · 2015
Earlier work this paper cites.
Siamese neural networks for one-shot image recognition
Gregory Koch, Richard Zemel, and Ruslan Salakhutdinov · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation
A. Amidi, S. Amidi, D. Vlachakis, V. Megalooikonomou, N. Paragios, and E. Zacharaki · 2017
Cited alongside, same era.
Protein interface prediction using graph convolutional networks
A. Fout, J. Byrd, B. Shariat, and A. Ben-Hur · 2017
Cited alongside, same era.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Cited alongside, same era.
DeepSite: protein-binding site predictor using 3D-convolutional neural networks
J Jiménez, S Doerr, G Martínez-Rosell, and A S Rose · 2017
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
Cited alongside, same era.
Graph U-nets
Hongyang Gao and Shuiwang Ji · 2019
Later among the works it cites.
Structure-based function prediction using graph convolutional networks
V. Gligorijevic, P. D. Renfrew, T. Kosciolek, J. K. Leman, K. Cho, T. Vatanen, D. Berenberg, B. Taylor, I. M. Fisk, R. J. Xavier, R. Knight, and R. Bonneau · 2019
Later among the works it cites.
Generative models for graph-based protein design
John Ingraham, Vikas Garg, Regina Barzilay, and Tommi Jaakkola · 2019
Later among the works it cites.
DeepGOPlus: improved protein function prediction from sequence
Maxat Kulmanov and Robert Hoehndorf · 2019
Later among the works it cites.
Evaluating protein transfer learning with tape
Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Xi Chen, John Canny, Pieter Abbeel, and Yun S. Song · 2019
Later among the works it cites.
Kpconv: Flexible and deformable convolution for point clouds
Hugues Thomas, Charles R Qi, Jean-Emmanuel Deschaud, Beatriz Marcotegui, François Goulette, and Leonidas J Guibas · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Maxat Kulmanov, Mohammed Asif Khan, and Robert Hoehndorf · 2017
Cited alongside, same era.
Protein–ligand scoring with convolutional neural networks
Matthew Ragoza, Joshua Hochuli, Elisa Idrobo, Jocelyn Sunseri, and David Ryan Koes · 2017
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
J. M Dana, A. Gutmanas, N. Tyagi, G. Qi, C. O’Donovan, M. Martin, and S. Velankar · 2018
Cited alongside, same era.
Deep convolutional networks for quality assessment of protein folds
Georgy Derevyanko, Sergei Grudinin, Yoshua Bengio, and Guillaume Lamoureux · 2018
Cited alongside, same era.
Flex-convolution (million-scale point-cloud learning beyond grid-worlds)
Fabian Groh, Patrick Wieschollek, and Hendrik Lensch · 2018
Cited alongside, same era.
Monte carlo convolution for learning on non-uniformly sampled point clouds
P. Hermosilla, T. Ritschel, P-P Vazquez, A. Vinacua, and T. Ropinski · 2018
Cited alongside, same era.
Deepsf: Deep convolutional neural network for mapping protein sequences to folds
J. Hou, B. Adhikari, and J. Cheng · 2018
Cited alongside, same era.
Later among the works it cites.
Wasserstein weisfeiler-lehman graph kernels
Matteo Togninalli, Elisabetta Ghisu, Felipe Llinares-López, Bastian Rieck, and Karsten Borgwardt · 2019
Later among the works it cites.
End-to-end learning on 3D protein structure for interface prediction
Raphael Townshend, Rishi Bedi, Patricia Suriana, and Ron Dror · 2019
Later among the works it cites.
Pointconv: Deep convolutional networks on 3d point clouds
Wenxuan Wu, Zhongang Qi, and Li Fuxin · 2019
Later among the works it cites.
Deformable filter convolution for point cloud reasoning
Yuwen Xiong, Mengye Ren, Renjie Liao, Kelvin Wong, and Raquel Urtasun · 2019
Later among the works it cites.
Hierarchical graph pooling with structure learning
Zhen Zhang, Jiajun Bu, Martin Ester, Jianfeng Zhang, Chengwei Yao, Zhi Yu, and Can Wang · 2019
Later among the works it cites.
GraphQA: Protein Model Quality Assessment using Graph Convolutional Networks
F. Baldassarre, D. M. Hurtado, A. Elofsson, and H. Azizpour · 2020
Closest in time.
A note on over-smoothing for graph neural networks
Chen Cai and Yusu Wang · 2020
Closest in time.
Revolutionary cryo-em is taking over structural biology
E. Callaway · 2020
Closest in time.
URL paperswithcode.com/sota/graph-classification-on-dd
Papers With Code · 2020
Closest in time.
Prottrans: Towards cracking the language of life’s code through self-supervised deep learning and high performance computing
A. Elnaggar, M. Heinzinger, C. Dallago, G. Rihawi, Y. Wang, L. Jones, T. Gibbs, T. Feher, C. Angerer, M. Steinegger, D. Bhowmik, and B. Rost · 2020
Closest in time.
Pre-training of deep bidirectional protein sequence representations with structural information
Seonwoo Min, Seunghyun Park, Siwon Kim, Hyun-Soo Choi, and Sungroh Yoon · 2020
Closest in time.
Universal self-attention network for graph classification
D. Q. Nguyen, T. D. Nguyen, and D. Phung · 2020
Closest in time.
Improved protein structure prediction using potentials from deep learning
A. W. Senior, R. Evans, J. Jumper, J. Kirkpatrick, L. Sifre, T. Green, C. Qin, A. Židek, A. W. R. Nelson, A. Bridgland, H. Penedones, S. Petersen, K. Simonyan, S. Crossan, P. Kohli, D. T. Jones, D. Silver, K. Kavukcuoglu, and D. Hassabis · 2020
Closest in time.
Udsmprot: universal deep sequence models for protein classification
Nils Strodthoff, Patrick Wagner, Markus Wenzel, and Wojciech Samek · 2020
Closest in time.