| Protein secondary structure prediction using deep convolutional neural fields S Wang, J Peng, J Ma, J Xu Scientific reports 6 (1), 1-11, 2016 | 694 | 2016 |
| Using deep learning to model the hierarchical structure and function of a cell J Ma, MK Yu, S Fong, K Ono, E Sage, B Demchak, R Sharan, T Ideker Nature methods 15 (4), 290-298, 2018 | 404 | 2018 |
| Predicting drug response and synergy using a deep learning model of human cancer cells BM Kuenzi, J Park, SH Fong, KS Sanchez, J Lee, JF Kreisberg, J Ma, ... Cancer cell 38 (5), 672-684. e6, 2020 | 351 | 2020 |
| High-resolution de novo structure prediction from primary sequence R Wu, F Ding, R Wang, R Shen, X Zhang, S Luo, C Su, Z Wu, Q Xie, ... BioRxiv, 2022.07. 21.500999, 2022 | 328 | 2022 |
| RaptorX server: a resource for template-based protein structure modeling M Källberg, G Margaryan, S Wang, J Ma, J Xu Protein structure prediction, 17-27, 2014 | 310 | 2014 |
| Protein structure alignment beyond spatial proximity S Wang, J Ma, J Peng, J Xu Scientific reports 3 (1), 1448, 2013 | 217 | 2013 |
| A 3D generative model for structure-based drug design S Luo, J Guan, J Ma, J Peng Advances in Neural Information Processing Systems 34, 6229-6239, 2021 | 206 | 2021 |
| Antigen-specific antibody design and optimization with diffusion-based generative models for protein structures S Luo, Y Su, X Peng, S Wang, J Peng, J Ma Advances in Neural Information Processing Systems 35, 9754-9767, 2022 | 195 | 2022 |
| Pocket2mol: Efficient molecular sampling based on 3d protein pockets X Peng, S Luo, J Guan, Q Xie, J Peng, J Ma International Conference on Machine Learning, 17644-17655, 2022 | 178 | 2022 |
| Protein threading using context-specific alignment potential J Ma, S Wang, F Zhao, J Xu Bioinformatics 29 (13), i257-i265, 2013 | 177 | 2013 |
| Visible machine learning for biomedicine KY Michael, J Ma, J Fisher, JF Kreisberg, BJ Raphael, T Ideker Cell 173 (7), 1562-1565, 2018 | 160 | 2018 |
| SARS-CoV-2 exacerbates proinflammatory responses in myeloid cells through C-type lectin receptors and Tweety family member 2 Q Lu, J Liu, S Zhao, MFG Castro, M Laurent-Rolle, J Dong, X Ran, ... Immunity 54 (6), 1304-1319. e9, 2021 | 147 | 2021 |
| 3d equivariant diffusion for target-aware molecule generation and affinity prediction J Guan, WW Qian, X Peng, Y Su, J Peng, J Ma arXiv preprint arXiv:2303.03543, 2023 | 143 | 2023 |
| Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning J Ma, S Wang, Z Wang, J Xu Bioinformatics 31 (21), 3506-3513, 2015 | 143 | 2015 |
| Robust single-cell Hi-C clustering by convolution-and random-walk–based imputation J Zhou, J Ma, Y Chen, C Cheng, B Bao, J Peng, TJ Sejnowski, JR Dixon, ... Proceedings of the National Academy of Sciences 116 (28), 14011-14018, 2019 | 141 | 2019 |
| Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients J Ma, SH Fong, Y Luo, CJ Bakkenist, JP Shen, S Mourragui, LFA Wessels, ... Nature Cancer 2 (2), 233-244, 2021 | 139 | 2021 |
| Breaking the limit of graph neural networks by improving the assortativity of graphs with local mixing patterns S Suresh, V Budde, J Neville, P Li, J Ma Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data …, 2021 | 138 | 2021 |
| AUCpreD: proteome-level protein disorder prediction by AUC-maximized deep convolutional neural fields S Wang, J Ma, J Xu Bioinformatics 32 (17), i672-i679, 2016 | 132 | 2016 |
| Instaflow: One step is enough for high-quality diffusion-based text-to-image generation X Liu, X Zhang, J Ma, J Peng The Twelfth International Conference on Learning Representations, 2023 | 114 | 2023 |
| When causal inference meets deep learning Y Luo, J Peng, J Ma Nature Machine Intelligence 2 (8), 426-427, 2020 | 111 | 2020 |
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