Jianzhu Ma
Cited by
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Protein secondary structure prediction using deep convolutional neural fields
S Wang, J Peng, J Ma, J Xu
Scientific reports 6 (1), 1-11, 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
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
RaptorX server: a resource for template-based protein structure modeling
M Kšllberg, G Margaryan, S Wang, J Ma, J Xu
Protein structure prediction, 17-27, 2014
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
Protein structure alignment beyond spatial proximity
S Wang, J Ma, J Peng, J Xu
Scientific reports 3 (1), 1448, 2013
Protein threading using context-specific alignment potential
J Ma, S Wang, F Zhao, J Xu
Bioinformatics 29 (13), i257-i265, 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
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
Visible machine learning for biomedicine
KY Michael, J Ma, J Fisher, JF Kreisberg, BJ Raphael, T Ideker
Cell 173 (7), 1562-1565, 2018
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
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
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
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
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
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
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
A conditional neural fields model for protein threading
J Ma, J Peng, S Wang, J Xu
Bioinformatics 28 (12), i59-i66, 2012
When causal inference meets deep learning
Y Luo, J Peng, J Ma
Nature Machine Intelligence 2 (8), 426-427, 2020
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
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Articles 1–20