Lucy J. Colwell
Molecular Biology · 2011–2024
A researcher in the Science corpus: 19 papers; trusts 92 researchers and is trusted by 42 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 457 citations into it; #911 of 17,773 by trust here, #1,319 by citations
- Computational Biology & Bioinformatics — 175 citations into it; #129 of 660 by trust here, #124 by citations
- Materials Chemistry — 136 citations into it
- Artificial Intelligence — 106 citations into it; #34,133 of 35,345 by trust here, #4,979 by citations
- Genetics — 89 citations into it; #3,613 of 6,854 by trust here, #4,626 by citations
- Spectroscopy — 62 citations into it
- Computer Vision and Pattern Recognition — 50 citations into it; #20,794 of 21,575 by trust here, #3,955 by citations
- Model Architectures — 49 citations into it; #2,156 of 2,278 by trust here, #513 by citations
Whom Lucy J. Colwell trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yang Zhang
- Martin Weigt
- John Moult
- Christopher Sander
- Adam T. Zemla
- Jeffrey Skolnick
- Debora Marks
- S. W. Lockless
- Michael Remmert
- Olivier Rivoire
Who trusts Lucy J. Colwell overall
- Anne-Florence Bitbol
- Sheng Wang
- David T. Jones
- Thomas A. Hopf
- Sergey Ovchinnikov
- Debora Marks
- T. Kościółek
- Jinbo Xu
- Mirco Michel
- Santrupti Nerli
Papers from Lucy J. Colwell's perspective
Ordered by how strongly the researchers Lucy J. Colwell trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet classification with deep convolutional neural networks
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Rethinking Attention with Performers (2020) — 2,365 citations
- Protein 3D Structure Computed from Evolutionary Sequence Variation (2011) — 1,167 citations
- 3D structures of membrane proteins from genomic sequencing (2012) — 517 citations
- Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers (2020) — 96 citations
- Inferring interaction partners from protein sequences (2016) — 133 citations
- Prediction of multiple conformational states by combining sequence clustering with AlphaFold2 (2022) — 58 citations
- Using deep learning to annotate the protein universe (2019) — 304 citations
- Deep diversification of an AAV capsid protein by machine learning (2021) — 310 citations
- Protein Sectors: Statistical Coupling Analysis versus Conservation (2014) — 75 citations
- Predicting multiple conformations via sequence clustering and AlphaFold2 (2023) — 466 citations
- Power law tails in phylogenetic systems (2018) — 62 citations
- Optimal Design of Experiments by Combining Coarse and Fine Measurements. (2017) — 4 citations
- Predicting protein–ligand affinity with a random matrix framework (2016) — 32 citations
- InterPro: the protein sequence classification resource in 2025 (2024) — 798 citations
- InterPro in 2022 (2022) — 1,929 citations
- MGnify: the microbiome sequence data analysis resource in 2023 (2022) — 355 citations
- Model-based reinforcement learning for biological sequence design (2020) — 166 citations
- Predicting Functionally Informative Mutations in Escherichia coli BamA Using Evolutionary Covariance Analysis (2013) — 37 citations
- 3D Protein Structure Predicted from Sequence (2011) — 6 citations