曾坚阳 Jianyang Zeng

研究方向:包括计算生物学,机器学习和大数据分析,长期致力于人工智能和生命科学的交叉学科研究。共发表学术论文80余篇,其中通讯作者论文包括Nature Machine Intelligence、Nature Communications、Nature Computational Science、PNAS、Cell Systems、Nucleic Acids Research等,合作作者论文包括Nature等。成果获得”科学探索奖”、ESI高引论文、”吴文俊人工智能自然科学”三等奖、”中国生物信息学十大进展”、”中国生物信息学十大算法和工具”、世界人工智能大会青年优秀论文、国际会议ICIBM 2019最佳论文等荣誉。担任国际期刊IEEE/ACM Transactions on Computational Biology and Bioinformatics的编委、计算生物学领域的国际顶级会议ISMB、RECOMB程序委员会委员、Cell Systems的Advisory Board成员。课题组目前科研方向围绕AI for Life Sciences展开,包括高通量实验方法开发、多组学测序方法开发、基于生物大数据的人工智能/机器学习模型开发、AI驱动的新型治疗方法开发和生物学知识发现等。

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