Computational Analysis of Mouse piRNA Sequence and Biogenesis. - Doron Betel; Robert P. Sheridan; Debora S. Marks; Chris Sander (PLoS Comput. Biol., 2007)

The microRNA.org resource: targets and expression. - Doron Betel; Manda Wilson; Aaron Gabow; Debora S. Marks; Chris Sander (Nucleic Acids Res., 2008)

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)

miRcode: a map of putative microRNA target sites in the long non-coding transcriptome. - Ashwini Jeggari; Debora S. Marks; Erik Larsson (Bioinform., 2012)

PconsFold: improved contact predictions improve protein models. - Mirco Michel; Sikander Hayat; Marcin J. Skwark; Chris Sander; Debora S. Marks; Arne Elofsson (Bioinform., 2014)

FreeContact: fast and free software for protein contact prediction from residue co-evolution. - László Kaján; Thomas A. Hopf; Matú Kala; Debora S. Marks; Burkhard Rost (BMC Bioinform., 2014)

Structure, Dynamics and Implied Gating Mechanism of a Human Cyclic Nucleotide-Gated Channel. - Yana Gofman; Charlotta Schärfe; Debora S. Marks; Turkan Haliloglu; Nir Ben-Tal (PLoS Comput. Biol., 2014)

Inferring Pairwise Interactions from Biological Data Using Maximum-Entropy Probability Models. - Richard R. Stein; Debora S. Marks; Chris Sander (PLoS Comput. Biol., 2015)

Variational Inference for Sparse and Undirected Models. - John Ingraham; Debora S. Marks (ICML, 2017)

Population-specific design of de-immunized protein biotherapeutics. - Benjamin Schubert; Charlotta Schärfe; Pierre Dönnes; Thomas A. Hopf; Debora S. Marks; Oliver Kohlbacher (PLoS Comput. Biol., 2018)

Learning Protein Structure with a Differentiable Simulator. - John Ingraham; Adam J. Riesselman; Chris Sander; Debora S. Marks (ICLR, 2019)

The EVcouplings Python framework for coevolutionary sequence analysis. - Thomas A. Hopf; Anna G. Green; Benjamin Schubert; Sophia Mersmann; Charlotta Schärfe; John Ingraham; Ágnes Tóth-Petróczy; Kelly Brock; Adam J. Riesselman; Perry Palmedo; Chan Kang; Robert P. Sheridan; et al. (Bioinform., 2019)

A Structured Observation Distribution for Generative Biological Sequence Prediction and Forecasting. - Eli N. Weinstein; Debora S. Marks (ICML, 2021)

A generative nonparametric Bayesian model for whole genomes. - Alan Nawzad Amin; Eli N. Weinstein; Debora S. Marks (NeurIPS, 2021)

Optimal Design of Stochastic DNA Synthesis Protocols based on Generative Sequence Models. - Eli N. Weinstein; Alan Nawzad Amin; Will S. Grathwohl; Daniel Kassler; Jean Disset; Debora S. Marks (AISTATS, 2022)

Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. - Pascal Notin; Mafalda Dias; Jonathan Frazer; Javier Marchena-Hurtado; Aidan N. Gomez; Debora S. Marks; Yarin Gal (ICML, 2022)

Non-identifiability and the Blessings of Misspecification in Models of Molecular Fitness. - Eli N. Weinstein; Alan Nawzad Amin; Jonathan Frazer; Debora S. Marks (NeurIPS, 2022)

RITA: a Study on Scaling Up Generative Protein Sequence Models. - Daniel Hesslow; Niccoló Zanichelli; Pascal Notin; Iacopo Poli; Debora S. Marks (CoRR, 2022)

PDBe-KB: collaboratively defining the biological context of structural data. - Mihaly Varadi; Stephen Anyango; David R. Armstrong; John M. Berrisford; Preeti Choudhary; Mandar S. Deshpande; Nurul Nadzirin; Sreenath Nair; Lukás Pravda; Ahsan Tanweer; Bissan Al-Lazikani; Claudia Andreini; et al. (Nucleic Acids Res., 2022)

A Kernelized Stein Discrepancy for Biological Sequences. - Alan Nawzad Amin; Eli N. Weinstein; Debora Susan Marks (ICML, 2023)

ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design. - Pascal Notin; Aaron Kollasch; Daniel Ritter 006; Lood van Niekerk; Steffanie Paul; Han Spinner; Nathan J. Rollins; Ada Shaw; Rose Orenbuch; Ruben Weitzman; Jonathan Frazer; Mafalda Dias; et al. (NeurIPS, 2023)

ProteinNPT: Improving protein property prediction and design with non-parametric transformers. - Pascal Notin; Ruben Weitzman; Debora S. Marks; Yarin Gal (NeurIPS, 2023)

Biological Sequence Kernels with Guaranteed Flexibility. - Alan Nawzad Amin; Eli Nathan Weinstein; Debora Susan Marks (CoRR, 2023)

Scientific discovery in the age of artificial intelligence. - Hanchen Wang 002; Tianfan Fu; Yuanqi Du; Wenhao Gao 001; Kexin Huang; Ziming Liu; Payal Chandak; Shengchao Liu; Peter Van Katwyk; Andreea Deac; Anima Anandkumar; Karianne Bergen; et al. (Nat., 2023)

Kernel-Based Evaluation of Conditional Biological Sequence Models. - Pierre Glaser; Steffanie Paul; Alissa M. Hummer; Charlotte M. Deane; Debora Susan Marks; Alan Nawzad Amin (ICML, 2024)

Multi-Scale Representation Learning for Protein Fitness Prediction. - Zuobai Zhang; Pascal Notin; Yining Huang; Aurélie C. Lozano; Vijil Chenthamarakshan; Debora S. Marks; Payel Das; Jian Tang 005 (NeurIPS, 2024)

Multi-megabase scale genome interpretation with genetic language models. - Frederik Träuble; Lachlan Stuart; Andreas Georgiou; Pascal Notin; Arash Mehrjou; Ron Schwessinger; Mathieu Chevalley; Kim Branson 001; Bernhard Schölkopf; Cornelia van Duijn; Debora S. Marks; Patrick Schwab (CoRR, 2025)

Protriever: End-to-End Differentiable Protein Homology Search for Fitness Prediction. - Ruben Weitzman; Peter Mørch Groth; Lood van Niekerk; Aoi Otani; Yarin Gal; Debora S. Marks; Pascal Notin (CoRR, 2025)