Eric W. Bell

Molecular Biology · 2019–2022

A researcher in the Science corpus: 6 papers; trusts 58 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 199 citations into it; #8,854 of 17,773 by trust here, #10,193 by citations
• Computational Biology & Bioinformatics — 111 citations into it
• Materials Chemistry — 98 citations into it; #8,844 of 11,513 by trust here, #10,245 by citations
• Computational Theory and Mathematics — 27 citations into it
• Model Architectures — 15 citations into it
• Spectroscopy — 14 citations into it
• Artificial Intelligence — 12 citations into it
• Computational Chemistry & Drug Discovery — 12 citations into it

Whom Eric W. Bell trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Yang Zhang
2. David T. Jones
3. Sitao Wu
4. Chengxin Zhang
5. Yang Li
6. Jeffrey Skolnick
7. Johannes Söding
8. Luigi Pietro Cordella
9. Jianyi Yang
10. Eric F. Pettersen

Who trusts Eric W. Bell overall

1. Robin Pearce
2. Wei Zheng
3. Hannes Stärk
4. Yang Zhang
5. Shaun M. Kandathil

Papers from Eric W. Bell's perspective

Ordered by how strongly the researchers Eric W. Bell trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Attention is All you Need (Neural Information Processing Systems)
4. ImageNet classification with deep convolutional neural networks
5. HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment
6. Automatic differentiation in PyTorch
7. TM-align: a protein structure alignment algorithm based on the TM-score
8. Identity Mappings in Deep Residual Networks
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Comparative protein modelling by satisfaction of spatial restraints.

Their papers

• Ensembling multiple raw coevolutionary features with deep residual neural networks for contact-map prediction in CASP13 (2019) — 88 citations
• Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks (2020) — 72 citations
• Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations (2021) — 502 citations
• EDock: blind protein–ligand docking by replica-exchange monte carlo simulation (2020) — 75 citations
• DockRMSD: an open-source tool for atom mapping and RMSD calculation of symmetric molecules through graph isomorphism (2019) — 406 citations
• I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction (2022) — 417 citations