| Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk J Zhou, CL Theesfeld, K Yao, KM Chen, AK Wong, OG Troyanskaya Nature genetics 50 (8), 1171-1179, 2018 | 588 | 2018 |
| A sequence-based global map of regulatory activity for deciphering human genetics KM Chen, AK Wong, OG Troyanskaya, J Zhou Nature genetics 54, 940-949, 2022 | 165 | 2022 |
| Genomic analyses implicate noncoding de novo variants in congenital heart disease F Richter, SU Morton, SW Kim, A Kitaygorodsky, LK Wasson, KM Chen, ... Nature genetics 52 (8), 769-777, 2020 | 143 | 2020 |
| Selene: a PyTorch-based deep learning library for sequence data KM Chen, EM Cofer, J Zhou, OG Troyanskaya Nature methods 16 (4), 315-318, 2019 | 130 | 2019 |
| Unsupervised extraction of stable expression signatures from public compendia with an ensemble of neural networks J Tan, G Doing, KA Lewis, CE Price, KM Chen, KC Cady, B Perchuk, ... Cell systems 5 (1), 63-71. e6, 2017 | 86 | 2017 |
| Genome-wide landscape of RNA-binding protein target site dysregulation reveals a major impact on psychiatric disorder risk CY Park, J Zhou, AK Wong, KM Chen, CL Theesfeld, RB Darnell, ... Nature genetics 53 (2), 166-173, 2021 | 69 | 2021 |
| PathCORE-T: identifying and visualizing globally co-occurring pathways in large transcriptomic compendia KM Chen, J Tan, GP Way, G Doing, DA Hogan, CS Greene BioData mining 11 (1), 1-20, 2018 | 16 | 2018 |