曾坚阳

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。

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