Not Just a Black Box: Learning Important Features Through Propagating Activation Differences. - Avanti Shrikumar; Peyton Greenside; Anna Shcherbina; Anshul Kundaje (CoRR, 2016)

Learning Important Features Through Propagating Activation Differences. - Avanti Shrikumar; Peyton Greenside; Anshul Kundaje (ICML, 2017)

Prediction of protein-ligand interactions from paired protein sequence motifs and ligand substructures. - Peyton Greenside; Maureen Hillenmeyer; Anshul Kundaje (PSB, 2018)

Discovering epistatic feature interactions from neural network models of regulatory DNA sequences. - Peyton Greenside; Tyler Shimko; Polly Fordyce; Anshul Kundaje (Bioinform., 2018)

CrowdVariant: a crowdsourcing approach to classify copy number variants. - Peyton Greenside; Justin M. Zook; Marc Salit; Madeleine L. Cule; Ryan Poplin; Mark A. DePristo (PSB, 2019)

A Hierarchical Approach to Scaling Batch Active Search Over Structured Data. - Vivek Myers; Peyton Greenside (CoRR, 2020)

Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders. - Samuel Stanton; Wesley J. Maddox; Nate Gruver; Phillip M. Maffettone; Emily Delaney; Peyton Greenside; Andrew Gordon Wilson (ICML, 2022)

Inverse Protein Folding Using Deep Bayesian Optimization. - Natalie Maus; Yimeng Zeng; Daniel Allen Anderson; Phillip M. Maffettone; Aaron Solomon; Peyton Greenside; Osbert Bastani; Jacob R. Gardner (CoRR, 2023)

Generative Humanization for Therapeutic Antibodies. - Cade Gordon; Aniruddh Raghu; Peyton Greenside; Hunter Elliott (CoRR, 2024)

Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences. - Alan Nawzad Amin; Nate Gruver; Yilun Kuang; Yucen Lily Li; Hunter Elliott; Calvin McCarter; Aniruddh Raghu; Peyton Greenside; Andrew Gordon Wilson (ICLR, 2025)