Lewis Moffat

Molecular Biology · 2018–2021

A researcher in the Science corpus: 3 papers; trusts 26 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 126 citations into it; #6,520 of 17,773 by trust here, #9,727 by citations
• Computational Biology & Bioinformatics — 59 citations into it; #498 of 660 by trust here, #529 by citations
• Materials Chemistry — 41 citations into it; #5,193 of 11,513 by trust here, #10,845 by citations
• Artificial Intelligence — 32 citations into it
• Generative Models — 17 citations into it; #1,636 of 1,956 by trust here, #1,315 by citations
• Computational Theory and Mathematics — 16 citations into it
• Computer Vision and Pattern Recognition — 15 citations into it
• Representation & Self-Supervised Learning — 10 citations into it

Whom Lewis Moffat trusts overall

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

1. Yang Zhang
2. Martin Steinegger
3. Joe G. Greener
4. Johannes Söding
5. David E. Kim
6. Ivan V. Anishchenko
7. Milot Mirdita
8. Michael Remmert
9. Geoffrey E. Hinton
10. Ashish Vaswani

Who trusts Lewis Moffat overall

1. Kevin Kaichuang Yang
2. Michael Jendrusch
3. Casper A. Goverde
4. B. Wicky
5. Niccoló Zanichelli
6. Mostafa Karimi
7. Jan O. Korbel
8. Wenhao Gao
9. J. Trinquier
10. Benedict Wolf

Papers from Lewis Moffat's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Design of metalloproteins and novel protein folds using variational autoencoders (2018) — 140 citations
• Using AlphaFold for Rapid and Accurate Fixed Backbone Protein Design (2021) — 30 citations
• A guide to machine learning for biologists (2021) — 1,713 citations