报告题目：Machine Learning in Species Delimitation
报告时间：2025年6月11日（星期三），上午9:30
报告地点：生科院1#109会议室
主办单位：生命科学学院、江苏省比较基因组学重点实验室、江苏省基因组学国际联合研究中心、华体会平台科学技术研究院
报告人简介：
刘亮，美国佐治亚大学统计系暨生物信息研究所教授。国际分子系统发育基因组学研究领域新型物种树方法的创始人之一，曾获2008年度国际系统生物学家协会优秀科研奖。长期担任Systematic Biology, Bioinformatics, Journal of Mathematic Biology, Molecular Biology and Evolution, Molecular Ecology 等国际学术期刊的评委，在Science、PNAS、National Science Review, Systematic Biology、Molecular Biology and Evolution、Bioinformatics等国际学术期刊发表论文80余篇，论文总引用次数约3.5万余次，单篇论文最高引用2.4万余次。担任美国国家自然科学基金委员会二审评委。
报告摘要:
Species delimitation is a fundamental task in systematics and biodiversity research, yet it remains a challenging endeavor due to the complex nature of evolutionary processes and the limitations of traditional analytical techniques. Recent advances in machine learning (ML) have significantly enhanced the field of species delimitation by introducing powerful computational tools capable of analyzing complex, high-dimensional datasets. ML algorithms provide a flexible and scalable framework for identifying patterns in genomic and phenotypic data, overcoming many of the limitations associated with traditional taxonomic methods. This paper reviews current applications of ML in species delimitation, highlighting its capacity to resolve issues related to gene flow, incomplete lineage sorting, and cryptic diversity. We present simulation studies that demonstrate the effectiveness of ML approaches and discuss their advantages in improving the accuracy, objectivity, and efficiency of species boundary inference. Our findings underscore the transformative potential of ML in systematics and call for broader integration of these tools in taxonomic research.