Jianyang Zeng

Research Interests: Computational biology, machine learning, and big data analysis, with long-term dedication to interdisciplinary research in artificial intelligence and life sciences. Published over 80 academic papers, including corresponding author papers in Nature Machine Intelligence, Nature Communications, Nature Computational Science, PNAS, Cell Systems, Nucleic Acids Research, etc., and collaborative papers in Nature. Achievements include the “Xplorer Prize,” ESI Highly Cited Papers, Third Prize of “Wu Wenjun AI Natural Science Award,” “Top 10 Advances in Chinese Bioinformatics,” “Top 10 Algorithms and Tools in Chinese Bioinformatics,” Outstanding Young Scholar Paper at World Artificial Intelligence Conference, and Best Paper at ICIBM 2019. Serves as editorial board member of IEEE/ACM Transactions on Computational Biology and Bioinformatics, program committee member of top computational biology conferences ISMB and RECOMB, and Advisory Board member of Cell Systems. Current research focuses on AI for Life Sciences, including high-throughput experimental method development, multi-omics sequencing method development, AI/machine learning model development based on biological big data, AI-driven novel therapeutic method development, and biological knowledge discovery.

Li, H., Lei, Y., & Zeng, J. (2024). Revolutionizing biomolecular structure determination with artificial intelligence. National science review, 11(11), nwae339. https://doi.org/10.1093/nsr/nwae339

Min, Y., Wei, Y., Wang, P., Wang, X., Li, H., Wu, N., Bauer, S., Zheng, S., Shi, Y., Wang, Y., Wu, J., Zhao, D., & Zeng, J. (2024). From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning. Advanced science (Weinheim, Baden-Wurttemberg, Germany), 11(40), e2405404. https://doi.org/10.1002/advs.202405404

Wang, P., Wen, X., Li, H., Lang, P., Li, S., Lei, Y., Shu, H., Gao, L., Zhao, D., & Zeng, J. (2023). Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON. Nature communications, 14(1), 8459. https://doi.org/10.1038/s41467-023-44103-3

Li, H., Zhang, R., Min, Y., Ma, D., Zhao, D., & Zeng, J. (2023). A knowledge-guided pre-training framework for improving molecular representation learning. Nature communications, 14(1), 7568. https://doi.org/10.1038/s41467-023-43214-1

Peng X, Lei Y, Feng P, et al. Characterizing the interaction conformation between T-cell receptors and epitopes with deep learning[J]. Nature machine intelligence, 2023, 5(4): 395-407