Black Box Recursive Translations for Molecular Optimization. - Farhan N. Damani; Vishnu Sresht; Stephen Ra (CoRR, 2019)

Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data. - Aaron M. Smith; Jonathan R. Walsh; John Long; Craig B. Davis; Peter Henstock; Martin R. Hodge; Mateusz Maciejewski; Xinmeng Jasmine Mu; Stephen Ra; Shanrong Zhao; Daniel Ziemek; Charles K. Fisher (BMC Bioinform., 2020)

Multi-segment preserving sampling for deep manifold sampler. - Daniel Berenberg; Jae Hyeon Lee; Simon Kelow; Ji Won Park; Andrew M. Watkins; Vladimir Gligorijevic; Richard Bonneau; Stephen Ra; Kyunghyun Cho (CoRR, 2022)

PropertyDAG: Multi-objective Bayesian optimization of partially ordered, mixed-variable properties for biological sequence design. - Ji Won Park; Samuel Stanton; Saeed Saremi; Andrew M. Watkins; Henri Dwyer; Vladimir Gligorijevic; Richard Bonneau; Stephen Ra; Kyunghyun Cho (CoRR, 2022)

A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences. - Natasa Tagasovska; Nathan C. Frey; Andreas Loukas; Isidro Hötzel; Julien Lafrance-Vanasse; Ryan Lewis Kelly; Yan Wu 027; Arvind Rajpal; Richard Bonneau; Kyunghyun Cho; Stephen Ra; Vladimir Gligorijevic (CoRR, 2022)

Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling. - Romain Lopez; Natasa Tagasovska; Stephen Ra; Kyunghyun Cho; Jonathan K. Pritchard; Aviv Regev (CLeaR, 2023)

OpenProteinSet: Training data for structural biology at scale. - Gustaf Ahdritz; Nazim Bouatta; Sachin Kadyan; Lukas Jarosch; Daniel Berenberg; Ian Fisk; Andrew M. Watkins; Stephen Ra; Richard Bonneau; Mohammed AlQuraishi (NeurIPS, 2023)

3D molecule generation by denoising voxel grids. - Pedro O. Pinheiro; Joshua Rackers; Joseph Kleinhenz; Michael Maser; Omar Mahmood; Andrew M. Watkins; Stephen Ra; Vishnu Sresht; Saeed Saremi (NeurIPS, 2023)

Protein Discovery with Discrete Walk-Jump Sampling. - Nathan C. Frey; Daniel Berenberg; Karina Zadorozhny; Joseph Kleinhenz; Julien Lafrance-Vanasse; Isidro Hötzel; Yan Wu 027; Stephen Ra; Richard Bonneau; Kyunghyun Cho; Andreas Loukas; Vladimir Gligorijevic; et al. (ICLR, 2024)

Concept Bottleneck Generative Models. - Aya Abdelsalam Ismail; Julius Adebayo; Héctor Corrada Bravo; Stephen Ra; Kyunghyun Cho (ICLR, 2024)

BOtied: Multi-objective Bayesian optimization with tied multivariate ranks. - Ji Won Park; Natasa Tagasovska; Michael Maser; Stephen Ra; Kyunghyun Cho (ICML, 2024)

Antibody DomainBed: Out-of-Distribution Generalization in Therapeutic Protein Design. - Natasa Tagasovska; Ji Won Park; Matthieu Kirchmeyer; Nathan C. Frey; Andrew Martin Watkins; Aya Abdelsalam Ismail; Arian Rokkum Jamasb; Edith Lee; Tyler Bryson; Stephen Ra; Kyunghyun Cho (CoRR, 2024)

Blind Biological Sequence Denoising with Self-Supervised Set Learning. - Nathan H. Ng; Ji Won Park; Jae Hyeon Lee; Ryan Lewis Kelly; Stephen Ra; Kyunghyun Cho (Trans. Mach. Learn. Res., 2024)

Supervised Contrastive Block Disentanglement. - Taro Makino; Ji Won Park; Natasa Tagasovska; Takamasa Kudo; Paula Coelho; Jan-Christian Huetter; Heming Yao; Burkhard Hoeckendorf; Ana Carolina Leote; Stephen Ra; David Richmond; Kyunghyun Cho; et al. (CoRR, 2025)