学术报告报告题目：Topological modeling and analysis of complex data in biomolecules
报 告 人：夏克林 博士（南洋理工大学）
报告时间：2019年6月11日（星期二）上午10:00-11:00
报告地点：4号楼4131室
邀 请 人：李兵 教授
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数学学院
2019年6月6日
报告摘要：
The understanding of biomolecular structure, flexibility, function, and dynamics is one of the most challenging tasks in biological science. We have introduced persistent homology for extracting molecular topological fingerprints (MTFs) based on the persistence of molecular topological invariants. MTFs are utilized for protein characterization, identification, and classification. The multidimensional persistent homology is proposed and further used to quantitatively predict the stability of protein folding configurations generated by steered molecular dynamics. Further, we introduce multiresolution persistent homology to handle complex biomolecular data. The essential idea is to match the resolution with the scale of interest so as to represent large scale datasets with appropriate resolution. Finally, I will discuss the recent progress in topology based machine learning models and their applications in drug design. Essentially, topological invariant information, extracted from biomolecular structures, can be used as feature vectors for statistic learning models, such as SVM, Random forest, CNN, etc. These topology based models have delivered some of the best results in D3R international drug design competition.
报告人简介：
Dr. Kelin Xia obtained his PhD degree from the Chinese Academy of Sciences in Jan 2013. He was a visiting scholar in the Department of Mathematics, Michigan State University from Dec 2009-Dec 2012. From Jan 2013 to May 2016, he worked as a visiting assistant professor at Michigan State University. He joined Nanyang Technological University at Jun 2016. His research focused on scientific computation, mathematical molecular biology, and topological data analysis (TDA) of complex data in biomolecular systems.