The research interests of the Zeng lab mainly focus on computational biology, machine learning and big data analysis, particularly the intersection between artificial intelligence/machine learning and life sciences. He has published over 80 papers in the prominent journals and conferences of computational biology and related fields, including top conferences ISMB and RECOMB, and prestigious journals, such as Nature (as a coauthor), Nature Machine Intelligence, Nature Communications, Nature Computational Science, Cell Systems, PNAS, Nucleic Acids Research, PLOS Computational Biology and Bioinformatics. He has been awarded “The XPLORER PRIZE” in 2023, “The National Science Fund for Distinguished Young Scholars in China” in 2021, “Wu Wenjun AI Science and Technology Award (Natural Science track, Third Prize)" in 2019, and “Top-10 Chinese Bioinformatics Breakthroughs" by Journal of Genomics, Proteomics and Bioinformatics in 2018 and 2019. He has been invited as a program committee (PC) member for prestigious international conferences in computational biology, including ISMB and RECOMB. He is an associate editor of IEEE/ACM Transactions on Computational Biology and Bioinformatics, and an advisory board member of Cell Systems.

1. Peizhuo Wang, Xiao Wen, Han Li, Peng Lang, Shuya Li, Yipin Lei, Hantao Shu, Lin Gao, Dan Zhao*, and Jianyang Zeng*. A network-based framework for deciphering driver regulators of cell fate decisions from single-cell RNA-seq data. Nature Communications. 2023.

2. Han Li, Ruotian Zhang, Yaosen Min, Dacheng Ma, Dan Zhao*, and Jianyang Zeng*. A knowledge-guided pre-training framework for improving molecular representation learning. Nature Communications. 2023.

3. Xingang Peng, Yipin Lei, Peiyuan Feng, Lemei Jia, Jianzhu Ma, Dan Zhao*, and Jianyang Zeng*. Characterizing the interaction conformation between T cell receptors and epitopes with deep learning. Nature Machine Intelligence, 2023, 5, 395-4071.

4. Shuya Li, Tingzhong Tian, Ziting Zhang, Ziheng Zou, Dan Zhao*, Jianyang Zeng*. PocketAnchor: Learning Structure-based Pocket Representations for Protein-Ligand Interaction Prediction. Cell Systems. 2023. Cover Article.

5. Yipin Lei, Shuya Li, Ziyi Liu, Fangping Wan, Tingzhong Tian, Shao Li, Dan Zhao*, Jianyang Zeng*. A deep-learning framework for multi-level peptide-protein interaction prediction. Nature Communications, 2021, 12, 5465.

6. Dan Zhao, Weifan Xu, Xiaofan Zhang, XiaotingWang, Enming Yuan, Yuanpeng Xiong, Shenyang Wu, Shuya Li, Nian Wu, Tingzhong Tian, Xiaolong Feng, Hantao Shu, Peng Lang, Xiaokun Shen, Haitao Li, Pilong Li*, Jianyang Zeng*. Understanding the phase separation characteristics of nucleocapsid protein provides a new therapeutic opportunity against SARS-CoV-2. Protein & Cell, 2021, 12(9):734-740.

7. Hantao Shu, Jingtian Zhou, Qiuyu Lian, Han Li, Dan Zhao, Jianyang Zeng*, Jianzhu Ma*. Modeling gene regulatory networks using neural network architectures. Nature Computational Science, 2021, 1, 491-501.

8. Peiyuan Feng, An Xiao, Meng Fang, Fangping Wan, Shuya Li, Peng Lang, Dan Zhao*, Jianyang Zeng*. A novel machine learning based framework for modeling transcription elongation. Proceedings of the National Academy of Sciences (PNAS), 2021, 118(6), e2007450118.

9. Yiyue Ge, Tingzhong Tian, Sulin Huang, Fangping Wan, Jingxin Li, Shuya Li, Hui Yang, Lixiang Hong, Nian Wu, Enming Yuan, Lili Cheng, Yipin Lei, Hantao Shu, Xiaolong Feng, Ziyuan Jiang, Ying Chi, Xiling Guo, Lunbiao Cui, Liang Xiao, Zeng Li, Chunhao Yang, Zehong Miao, Haidong Tang, Ligong Chen, Hainian Zeng, Dan Zhao*, Fengcai Zhu*, Xiaokun Shen*, Jianyang Zeng*. A data-driven drug repositioning framework discovered a potential therapeutic agent targeting COVID-19. Signal Transduction and Targeted Therapy, 2021, 6, 165.

10. Lixiang Hong, Jinjian Lin, Shuya Li, FangpingWan, Hui Yang, Tao Jiang, Dan Zhao*, Jianyang Zeng*. A novel machine learning framework for automated biomedical relation extraction from large-scale literature repositories. Nature Machine Intelligence, 2020, 2, 347-355.

11. Shuya Li, Fangping Wan, Hantao Shu, Tao Jiang, Dan Zhao*, Jianyang Zeng*. MONN: A multi-objective neural network for predicting compound-protein interactions and affinities. Cell Systems, 2020, 10(4):308-322.  

12. Ahmed Abbas, Xuan He, Bin Zhou, Guangxiang Zhu, Zishan Ma, Jun-Tao Gao, Michael Q Zhang, Jianyang Zeng*. Integrating Hi-C and FISH data for modeling 3D organizations of chromosomes. Nature Communications, 2019, 10, 2049.

13. Guangxiang Zhu, Wenxuan Deng, Hailin Hu, Rui Ma, Sai Zhang, Jinglin Yang, Jian Peng, Tommy Kaplan, Jianyang Zeng*. Reconstructing spatial organizations of chromosomes through manifold learning. Nucleic Acids Research. 2018, 46 (8), e50.

14. Zhenhai Du, Hui Zheng, Bo Huang, Rui Ma, Jingyi Wu, Xianglin Zhang, Jing He, Yunlong Xiang, Qiujun Wang, Yuanyuan Li, Jing Ma, Xu Zhang, Ke Zhang, Yang Wang, Michael Q. Zhang, Juntao Gao, Jesse R. Dixon, Xiaowo Wang, Jianyang Zeng, Wei Xie*. Allelic reprogramming of 3D chromatin architecture during early mammalian development. Nature, 2017, 547, 232-235.

15. Yunan Luo, Xinbin Zhao, Jingtian Zhou, Jinling Yang, Yanqing Zhang, Wen-hua Kuang, Jian Peng*, Ligong Chen*, Jianyang Zeng*. A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nature Communications, 2017, 8, 573.

16. Sai Zhang, Hailin Hu, Jingtian Zhou, Xuan He, Tao Jiang, Jianyang Zeng*. Analysis of ribosome stalling and translation elongation dynamics by deep learning. Cell Systems, 2017, 5(3):212-220.

17. Sai Zhang, Jingtian Zhou, Hailin Hu, Haipeng Gong, Ligong Chen, Chao Cheng*, Jianyang Zeng*. A deep learning framework for modeling structural features of RNA-binding protein targets. Nucleic Acids Research, 2016, 44(4):e32.

18. Han Li, Dan Zhao*, Jianyang Zeng*. KPGT: knowledge-guided pre-training of graph transformer for molecular property prediction. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022, 857-867.

19. Yichao Zhou, Yuexin Wu, Jianyang Zeng*. Computational protein design uUsing AND/OR branch-and-bound search. International Conference on Research in Computational Molecular Biology (RECOMB), 2015.

20. Yichao Zhou, Jianyang Zeng*. Massively Parallel A* Search on a GPU. Proceeding of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI), 2015.

21. Yichao Zhou, Wei Xu, Bruce R. Donald, Jianyang Zeng*. An efficient parallel algorithm for accelerating computational protein design. Proceedings of the 22nd Annual International Conference on Intelligent Systems for Molecular Biology (ISMB), 2014.

22. Yuhao Wang, Jianyang Zeng*. Predicting drug-target interactions using restricted Boltzmann machines. Proceedings of the 21nd Annual International Conference on Intelligent Systems for Molecular Biology (ISMB), 2013.