Ma, Jianzhu

Large Scale Foundation Model on Single-cell Transcriptomics

Hao, M., Gong, J., Zeng, X., Liu, C., Guo, Y., Cheng, X., Wang, T., Ma, J., Song, L., & Zhang, X. (2023) · Nature Methods

Single-cell and spatial transcriptomicsGene expression and cancer classification

Cited by 639

Large-scale foundation model on single-cell transcriptomics

Hao, M., Gong, J., Zeng, X., Liu, C., Guo, Y., Cheng, X., Wang, T., Ma, J., Zhang, X., & Song, L. (2024) · Nature Methods

Single-cell and spatial transcriptomicsGene expression and cancer classification

Cited by 628

Predicting drug response and synergy using a deep learning model of human cancer cells

Kuenzi, B. M., Park, J., Fong, S. H., Sanchez, K. S., Lee, J., Kreisberg, J. F., Ma, J., & Ideker, T. (2020) · Cancer Cell

Computational Drug Discovery MethodsMachine Learning in Healthcare

Cited by 544

Using deep learning to model the hierarchical structure and function of a cell

Ma, J., Yu, M. K., Fong, S., Ono, K., Sage, E., Demchak, B., Sharan, R., & Ideker, T. (2018) · Nature Methods

Gene Regulatory Network AnalysisMicrobial Metabolic Engineering and Bioproduction

Cited by 435

High-resolution de novo structure prediction from primary sequence

Wu, R., Ding, F., Wang, R., Shen, R., Zhang, X., Luo, S., Su, C., Wu, Z., Xie, Q., Berger, B., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory)

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 392

Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients

Ma, J., Fong, S. H., Luo, Y., Bakkenist, C. J., Shen, J. P., Mourragui, S., Wessels, L. F. A., Hafner, M., Sharan, R., Peng, J., et al. (2021) · Nature Cancer

Computational Drug Discovery MethodsDomain Adaptation and Few-Shot Learning

Cited by 180

Robust single-cell Hi-C clustering by convolution- and random-walk-based imputation

Zhou, J., Ma, J., Chen, Y., Cheng, C., Bao, B., Peng, J., Sejnowski, T. J., Dixon, J. R., & Ecker, J. R. (2019) · Proceedings of the National Academy of Sciences

Genomics and Chromatin DynamicsSingle-cell and spatial transcriptomics

Cited by 180

Visible Machine Learning for Biomedicine

Yu, M. K., Ma, J., Fisher, J., Kreisberg, J. F., Raphael, B. J., & Ideker, T. (2018) · Cell

Machine Learning in HealthcareExplainable Artificial Intelligence (XAI)

Cited by 162

Deep learning guided optimization of human antibody against SARS-CoV-2 variants with broad neutralization

Shan, S., Luo, S., Yang, Z., Hong, J., Su, Y., Ding, F., Fu, L., Li, C., Chen, P., Ma, J., et al. (2022) · Proceedings of the National Academy of Sciences

SARS-CoV-2 and COVID-19 Researchvaccines and immunoinformatics approaches

Cited by 157

Antigen-Specific Antibody Design and Optimization with Diffusion-Based Generative Models for Protein Structures

Luo, S., Su, Y., Peng, X., Wang, S., Peng, J., & Ma, J. (2022) · neural information processing systems

vaccines and immunoinformatics approachesMonoclonal and Polyclonal Antibodies Research

Cited by 151

Single-cell multiomics analysis reveals regulatory programs in clear cell renal cell carcinoma

Long, Z., Sun, C., Tang, M., Wang, Y., Ma, J., Yu, J., Wei, J., Ma, J., Wang, B., Xie, Q., et al. (2022) · Cell Discovery

Renal cell carcinoma treatmentSingle-cell and spatial transcriptomics

Cited by 109

A multi-scale map of cell structure fusing protein images and interactions

Qin, Y., Huttlin, E. L., Winsnes, C. F., Gosztyla, M. L., Wacheul, L., Kelly, M. R., Blue, S. M., Zheng, F., Chen, M., Schaffer, L. V., et al. (2021) · Nature

Bioinformatics and Genomic NetworksAdvanced Proteomics Techniques and Applications

Cited by 82

Peptide Design through Binding Interface Mimicry

Kong, X., Jiao, R., Lin, H., Guo, R., Huang, W., Ma, W.-Y., Wang, Z., Liu, Y., & Ma, J. (2025) · Nature Biomedical Engineering

Chemical Synthesis and Analysisvaccines and immunoinformatics approaches

Cited by 30

Escaping the drug-bias trap: using debiasing design to improve interpretability and generalization of drug-target interaction prediction

Zhang, P.-D., Ma, J., & Chen, T. (2024) · IEEE Transactions on Computational Biology and Bioinformatics

Computational Drug Discovery MethodsMachine Learning in Bioinformatics

Cited by 13

Escaping the Drug-Bias Trap: Using Debiasing Design to Improve Interpretability and Generalization of Drug-Target Interaction Prediction

Zhang, P., Ma, J., & Chen, T. (2025) · IEEE Transactions on Computational Biology and Bioinformatics

Computational Drug Discovery MethodsMachine Learning in Bioinformatics

Cited by 13

FlowDesign: Improved Design of Antibody CDRs Through Flow Matching and Better Prior Distributions

Wu, J., Kong, X., Sun, N., Wei, J., Shan, S., Feng, F., Wu, F., Peng, J., Zhang, L., Liu, Y., et al. (2024) · Cell Systems

Monoclonal and Polyclonal Antibodies ResearchProtein Structure and Dynamics

Cited by 11

FlowDesign: Improved design of antibody CDRs through flow matching and better prior distributions

Wu, J., Kong, X., Sun, N., Wei, J., Shan, S., Feng, F., Wu, F., Peng, J., Zhang, L., Liu, Y., et al. (2025) · Cell Systems

Monoclonal and Polyclonal Antibodies ResearchProtein Structure and Dynamics

Cited by 11