| Evolutionary-scale prediction of atomic-level protein structure with a language model Z Lin, H Akin, R Rao, B Hie, Z Zhu, W Lu, N Smetanin, R Verkuil, O Kabeli, ... Science 379 (6637), 1123-1130, 2023 | 1302* | 2023 |
| Efficient integration of heterogeneous single-cell transcriptomes using Scanorama B Hie, B Bryson, B Berger Nature biotechnology 37 (6), 685-691, 2019 | 635 | 2019 |
| Learning inverse folding from millions of predicted structures C Hsu, R Verkuil, J Liu, Z Lin, B Hie, T Sercu, A Lerer, A Rives International Conference on Machine Learning, 8946-8970, 2022 | 230 | 2022 |
| Learning the language of viral evolution and escape B Hie, ED Zhong, B Berger, B Bryson Science 371 (6526), 284-288, 2021 | 229 | 2021 |
| Efficient evolution of human antibodies from general protein language models BL Hie, VR Shanker, D Xu, TUJ Bruun, PA Weidenbacher, S Tang, W Wu, ... Nature Biotechnology, 1-9, 2023 | 127 | 2023 |
| Predicting the mutational drivers of future SARS-CoV-2 variants of concern MC Maher, I Bartha, S Weaver, J Di Iulio, E Ferri, L Soriaga, FA Lempp, ... Science translational medicine 14 (633), eabk3445, 2022 | 127 | 2022 |
| Leveraging Uncertainty in Machine Learning Accelerates Biological Discovery and Design B Hie, BD Bryson, BA Berger Cell Systems 11 (5), 461-477.e9, 2020 | 122 | 2020 |
| Pooled ChIP-seq links variation in transcription factor binding to complex disease risk AK Tehranchi, M Myrthil, T Martin, BL Hie, D Golan, HB Fraser Cell 165 (3), 730-741, 2016 | 119 | 2016 |
| Geometric sketching compactly summarizes the single-cell transcriptomic landscape B Hie, H Cho, B DeMeo, B Bryson, B Berger Cell systems 8 (6), 483-493. e7, 2019 | 104 | 2019 |
| Computational Methods for Single-Cell RNA Sequencing B Hie, J Peters, SK Nyquist, AK Shalek, B Berger, BD Bryson Annual Review of Biomedical Data Science 3, 2020 | 91 | 2020 |
| Evolutionary velocity with protein language models predicts evolutionary dynamics of diverse proteins BL Hie, KK Yang, PS Kim Cell Systems 13 (4), 274-285. e6, 2022 | 72* | 2022 |
| Adaptive machine learning for protein engineering BL Hie, KK Yang Current opinion in structural biology 72, 145-152, 2022 | 70 | 2022 |
| Realizing private and practical pharmacological collaboration B Hie, H Cho, B Berger Science 362 (6412), 347-350, 2018 | 63 | 2018 |
| Fine-mapping cis-regulatory variants in diverse human populations A Tehranchi, B Hie, M Dacre, I Kaplow, K Pettie, P Combs, HB Fraser eLife 8, e39595, 2019 | 60 | 2019 |
| Schema: metric learning enables interpretable synthesis of heterogeneous single-cell modalities R Singh, BL Hie, A Narayan, B Berger Genome biology 22 (1), 131, 2021 | 36* | 2021 |
| A high-level programming language for generative protein design B Hie, S Candido, Z Lin, O Kabeli, R Rao, N Smetanin, T Sercu, A Rives bioRxiv, 2022.12. 21.521526, 2022 | 21 | 2022 |
| Realizing private and practical pharmacological collaboration using a neural network architecture configured for reduced computation overhead B Hie, BB Leighton, H Cho US Patent 11,450,439, 2022 | 7 | 2022 |
| Coexpression enables multi-study cellular trajectories of development and disease B Hie, H Cho, B Bryson, B Berger bioRxiv, 719088, 2020 | 7* | 2020 |
| Learning mutational semantics B Hie, E Zhong, B Bryson, B Berger Advances in Neural Information Processing Systems 33, 9109-9121, 2020 | 6 | 2020 |
| Machine Learning for Protein Engineering KE Johnston, C Fannjiang, BJ Wittmann, BL Hie, KK Yang, Z Wu Machine Learning in Molecular Sciences, 277-311, 2023 | 5 | 2023 |
