Systematic Evaluation of AI Methods for Anticancer Drug Target Discovery by Zheng Jie’s Team and Collaborators
In October 2024, a research paper titled “Benchmarking Machine Learning Methods for Synthetic Lethality Prediction in Cancer” was published in Nature Communications by the research group led by Zheng Jie from the School of Information Science and Technology at ShanghaiTech University, in collaboration with other partners. The study systematically compared the performance of 12 cutting-edge machine learning methods in predicting synthetic lethality (SL) cancer drug targets across multiple designed scenarios. This work provides scientists with a comprehensive guide to selecting the most suitable SL prediction tools, thereby advancing the development of precision anticancer drugs. Among the 12 methods, 10 were co-developed by the paper’s corresponding authors Zheng Jie and Wu Min, demonstrating state-of-the-art performance and establishing them as leaders in machine learning research for synthetic lethality prediction.
Authorship and Affiliations: The paper’s first author is Yimiao Feng, a 2022 Ph.D. candidate jointly supervised by the School of Information Science and Technology (SIST) at ShanghaiTech University and the Lingang Laboratory (Zheng Jie’s group). Co-authors include Yahui Long, Assistant Researcher at the Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore; Quan Li, Faculty Researcher at SIST; and SIST master’s students He Wang and Yang Ouyang (Quan Li’s group).
Contributors: SIST graduate students Weifan Mao, Zhen Yue, Siyu Tao, and Yang Yang provided technical support. Computational resources were supplied by ShanghaiTech University’s Library and Information Center and Ningxia Xiyun Computing Technology Co., Ltd.
Funding and Origins: This work originated from a course project in CS286 (AI for Science & Engineering). The co-corresponding authors are Min Wu, Principal Scientist at A*STAR’s Institute for Infocomm Research (I²R), Singapore, and Jie Zheng, Faculty Researcher at SIST. ShanghaiTech University is the primary affiliated institution.
