| Mutation effects predicted from sequence co-variation TA Hopf, JB Ingraham, FJ Poelwijk, CPI Schärfe, M Springer, C Sander, ... Nature Biotechnology 35 (2), 128-135, 2017 | 625 | 2017 |
| Deep generative models of genetic variation capture the effects of mutations AJ Riesselman, JB Ingraham, DS Marks Nature Methods 15, 816-822, 2018 | 556 | 2018 |
| Generative Models for Graph-Based Protein Design J Ingraham, VK Garg, R Barzilay, T Jaakkola Neural Information Processing Systems, 2019 | 470 | 2019 |
| Defining variant-resistant epitopes targeted by SARS-CoV-2 antibodies: A global consortium study KM Hastie, H Li, D Bedinger, SL Schendel, SM Dennison, K Li, ... Science 374 (6566), 472-478, 2021 | 260 | 2021 |
| Basketball teams as strategic networks JH Fewell, D Armbruster, J Ingraham, A Petersen, JS Waters PloS one 7 (11), e47445, 2012 | 249 | 2012 |
| The EVcouplings Python framework for coevolutionary sequence analysis TA Hopf, AG Green, B Schubert, S Mersmann, CPI Schärfe, JB Ingraham, ... Bioinformatics 35 (9), 1582-1584, 2019 | 215 | 2019 |
| 3D RNA and Functional Interactions from Evolutionary Couplings C Weinreb, AJ Riesselman, JB Ingraham, T Gross, C Sander, DS Marks Cell 165 (4), 963-975, 2016 | 188 | 2016 |
| Illuminating protein space with a programmable generative model JB Ingraham, M Baranov, Z Costello, KW Barber, W Wang, A Ismail, ... Nature 623 (7989), 1070-1078, 2023 | 177 | 2023 |
| Learning Protein Structure with a Differentiable Simulator J Ingraham, A Riesselman, C Sander, D Marks International Conference on Learning Representations, 2019 | 162 | 2019 |
| Structured States of Disordered Proteins from Genomic Sequences A Toth-Petroczy, P Palmedo, J Ingraham, TA Hopf, B Berger, C Sander, ... Cell 167 (1), 158-170. e12, 2016 | 151 | 2016 |
| Galactose metabolic genes in yeast respond to a ratio of galactose and glucose R Escalante-Chong, Y Savir, SM Carroll, JB Ingraham, J Wang, CJ Marx, ... Proceedings of the National Academy of Sciences 112 (5), 1636-1641, 2015 | 139 | 2015 |
| Cellbox: Interpretable machine learning for perturbation biology with application to the design of cancer combination therapy B Yuan, C Shen, A Luna, A Korkut, DS Marks, J Ingraham, C Sander Cell systems 12 (2), 128-140. e4, 2021 | 110 | 2021 |
| Generating transition states of isomerization reactions with deep learning L Pattanaik, JB Ingraham, CA Grambow, WH Green Physical Chemistry Chemical Physics 22 (41), 23618-23626, 2020 | 51 | 2020 |
| Variational Inference for Sparse and Undirected Models J Ingraham, D Marks International Conference on Machine Learning, 2017 | 44* | 2017 |
| Simultaneous enhancement of multiple functional properties using evolution-informed protein design B Fram, Y Su, I Truebridge, AJ Riesselman, JB Ingraham, A Passera, ... Nature Communications 15 (1), 1-16, 2024 | 6 | 2024 |
| Antigen Binding Molecules Targeting SARS-CoV-2 G Grigoryan, J Ingraham, CL Leung, RS Federman, RJ Green, V Xue, ... US Patent App. 18/151,088, 2023 | 3 | 2023 |
| Probabilistic Models of Structure in Biological Sequences JB Ingraham PQDT-Global, 2018 | 2 | 2018 |
| Antigen binding molecules targeting SARS-CoV-2 G Grigoryan, J Ingraham US Patent 11,987,616, 2024 | | 2024 |
| Antigen binding molecules targeting SARS-CoV-2 G Grigoryan, J Ingraham, CL Leung, RS Federman, RJ Green, AH Ramos, ... US Patent 11,981,725, 2024 | | 2024 |
| Path analysis of vocally-mediated intergroup spacing strategies in mantled howling monkeys J Ingraham, AL Schreier AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 144, 173-174, 2011 | | 2011 |
Debora MarksSystems Biology, Harvard Medical School, Broad Institute of Harvard and MIT在 hms.harvard.edu 的电子邮件经过验证
