Assembly of Long Error-Prone Reads Using de Bruijn Graphs. - Yu Lin 001; Max W. Shen; Jeffrey Yuan; Mark Chaisson; Pavel A. Pevzner (RECOMB, 2016)

plasmidSPAdes: assembling plasmids from whole genome sequencing data. - Dmitry Antipov; Nolan Hartwick; Max W. Shen; Mikhail Raiko; Alla L. Lapidus; Pavel A. Pevzner (Bioinform., 2016)

Assembly of long error-prone reads using de Bruijn graphs. - Yu Lin 001; Jeffrey Yuan; Mikhail Kolmogorov; Max W. Shen; Mark Chaisson; Pavel A. Pevzner (Proc. Natl. Acad. Sci. USA, 2016)

Predictable and precise template-free CRISPR editing of pathogenic variants. - Max W. Shen; Mandana Arbab; Jonathan Y. Hsu; Daniel Worstell; Sannie J. Culbertson; Olga Krabbe; Christopher A. Cassa; David R. Liu; David K. Gifford; Richard I. Sherwood (Nat., 2018)

Machine learning based CRISPR gRNA design for therapeutic exon skipping. - Wilson Louie; Max W. Shen; Zakir Tahiry; Sophia Zhang; Daniel Worstell; Christopher A. Cassa; Richard I. Sherwood; David K. Gifford (PLoS Comput. Biol., 2021)

Detection of gene cis-regulatory element perturbations in single-cell transcriptomes. - Grace Hui Ting Yeo; Oscar Juez; Qing Chen; Budhaditya Banerjee; Lendy Chu; Max W. Shen; May Sabry; Ive Logister; Richard I. Sherwood; David K. Gifford (PLoS Comput. Biol., 2021)

Trust in AI: Interpretability is not necessary or sufficient, while black-box interaction is necessary and sufficient. - Max W. Shen (CoRR, 2022)

Conditional Diffusion with Less Explicit Guidance via Model Predictive Control. - Max W. Shen; Ehsan Hajiramezanali; Gabriele Scalia; Alex M. Tseng; Nathaniel Diamant; Tommaso Biancalani; Andreas Loukas (CoRR, 2022)

Hierarchically branched diffusion models for efficient and interpretable multi-class conditional generation. - Alex M. Tseng; Tommaso Biancalani; Max W. Shen; Gabriele Scalia (CoRR, 2022)

Towards Understanding and Improving GFlowNet Training. - Max W. Shen; Emmanuel Bengio; Ehsan Hajiramezanali; Andreas Loukas; Kyunghyun Cho; Tommaso Biancalani (ICML, 2023)

Score-based Explainability for Graph Representations. - Ehsan Hajiramezanali; Sepideh Maleki; Max W. Shen; Kangway V. Chuang; Tommaso Biancalani; Gabriele Scalia (Trans. Mach. Learn. Res., 2024)

Hierarchically branched diffusion models leverage dataset structure for class-conditional generation. - Alex M. Tseng; Max W. Shen; Tommaso Biancalani; Gabriele Scalia (Trans. Mach. Learn. Res., 2024)

Do we need equivariant models for molecule generation? - Ewa M. Nowara; Joshua Rackers; Patricia Suriana; Pan Kessel; Max Shen 002; Andrew Martin Watkins; Michael Maser (CoRR, 2025)