Action Potential Initiation in the Hodgkin-Huxley Model. - Lucy J. Colwell; Michael P. Brenner (PLoS Comput. Biol., 2009)

Charge as a Selection Criterion for Translocation through the Nuclear Pore Complex. - Lucy J. Colwell; Michael P. Brenner; Katharina Ribbeck (PLoS Comput. Biol., 2010)

3D Protein Structure Predicted from Sequence - Debora S. Marks; Lucy J. Colwell; Robert P. Sheridan; Thomas A. Hopf; Andrea Pagnani; Riccardo Zecchina; Chris Sander (CoRR, 2011)

Protein Sectors: Statistical Coupling Analysis versus Conservation. - Tiberiu Tesileanu; Lucy J. Colwell; Stanislas Leibler (PLoS Comput. Biol., 2015)

Using Attribution to Decode Dataset Bias in Neural Network Models for Chemistry. - Kevin McCloskey; Ankur Taly; Federico Monti; Michael P. Brenner; Lucy J. Colwell (CoRR, 2018)

Noisy, sparse, nonlinear: Navigating the Bermuda Triangle of physical inference with deep filtering. - Carl Poelking; Yehia Amar; Alexei A. Lapkin; Lucy J. Colwell (CoRR, 2019)

Model-based reinforcement learning for biological sequence design. - Christof Angermüller; David Dohan; David Belanger 002; Ramya Deshpande; Kevin Murphy 002; Lucy J. Colwell (ICLR, 2020)

Population-Based Black-Box Optimization for Biological Sequence Design. - Christof Angermüller; David Belanger 002; Andreea Gane; Zelda Mariet; David Dohan; Kevin Murphy 002; Lucy J. Colwell; D. Sculley (ICML, 2020)

Evaluating Attribution for Graph Neural Networks. - Benjamín Sánchez-Lengeling; Jennifer N. Wei; Brian K. Lee; Emily Reif; Peter Wang; Wesley Wei Qian; Kevin McCloskey; Lucy J. Colwell; Alexander B. Wiltschko (NeurIPS, 2020)

Computational approaches to therapeutic antibody design: established methods and emerging trends. - Richard A. Norman; Francesco Ambrosetti; Alexandre M. J. J. Bonvin; Lucy J. Colwell; Sebastian Kelm; Sandeep Kumar; Konrad Krawczyk (Briefings Bioinform., 2020)

Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers. - Krzysztof Choromanski; Valerii Likhosherstov; David Dohan; Xingyou Song; Jared Davis; Tamás Sarlós; David Belanger 002; Lucy J. Colwell; Adrian Weller (CoRR, 2020)

Critiquing Protein Family Classification Models Using Sufficient Input Subsets. - Brandon Carter 001; Maxwell L. Bileschi; Jamie Smith; Theo Sanderson; Drew Bryant; David Belanger 002; Lucy J. Colwell (J. Comput. Biol., 2020)

The Effect of Debiasing Protein-Ligand Binding Data on Generalization. - Vikram Sundar; Lucy J. Colwell (J. Chem. Inf. Model., 2020)

Rethinking Attention with Performers. - Krzysztof Marcin Choromanski; Valerii Likhosherstov; David Dohan; Xingyou Song; Andreea Gane; Tamás Sarlós; Peter Hawkins; Jared Quincy Davis; Afroz Mohiuddin; Lukasz Kaiser; David Benjamin Belanger; Lucy J. Colwell; et al. (ICLR, 2021)

Improving Protein Function Annotation via Unsupervised Pre-training: Robustness, Efficiency, and Insights. - David Dohan; Andreea Gane; Maxwell L. Bileschi; David Belanger 002; Lucy J. Colwell (KDD, 2021)

A roadmap for the functional annotation of protein families: a community perspective. - Valérie de Crécy-Lagard; Rocio Amorin de Hegedus; Cecilia N. Arighi; Jill Babor; Alex Bateman; Ian Blaby; Crysten Blaby-Haas; Alan J. Bridge; Stephen K. Burley; Stacey Cleveland; Lucy J. Colwell; Ana Conesa; et al. (Database J. Biol. Databases Curation, 2022)

Meaningful machine learning models and machine-learned pharmacophores from fragment screening campaigns. - Carl Poelking; Gianni Chessari; Christopher W. Murray; Richard J. Hall; Lucy J. Colwell; Marcel L. Verdonk (CoRR, 2022)

AVIDa-hIL6: A Large-Scale VHH Dataset Produced from an Immunized Alpaca for Predicting Antigen-Antibody Interactions. - Hirofumi Tsuruta; Hiroyuki Yamazaki; Ryota Maeda; Ryotaro Tamura; Jennifer N. Wei; Zelda E. Mariet; Poomarin Phloyphisut; Hidetoshi Shimokawa; Joseph R. Ledsam; Lucy J. Colwell; Akihiro Imura (NeurIPS, 2023)

InterPro in 2022. - Typhaine Paysan-Lafosse; Matthias Blum; Sara Chuguransky; Tiago Grego; Beatriz Lázaro Pinto; Gustavo A. Salazar; Maxwell L. Bileschi; Peer Bork; Alan J. Bridge; Lucy J. Colwell; Julian Gough; Daniel H. Haft; et al. (Nucleic Acids Res., 2023)

MGnify: the microbiome sequence data analysis resource in 2023. - Lorna J. Richardson; Ben Allen; Germana Baldi; Martin Beracochea; Maxwell L. Bileschi; Tony Burdett; Josephine Burgin; Juan Caballero; Guy Cochrane; Lucy J. Colwell; Tom Curtis; Alejandra Escobar-Zepeda; et al. (Nucleic Acids Res., 2023)