CBCB Seminar

March 11, 2024 3:30 PM

Ammon-Pinizzotto Biopharmaceutical Innovation (BPI) Building
Conference Room 140

Study of disease comorbidity based on incomplete human interactome with machine learning

Li Liao, PhD

Associate Professor
Computer and Information Sciences Department
University of Delaware

Abstract: Disease comorbidity presents challenges to both diagnosis and treatment. The genetic causes can be traced to mutations at individual genes and interaction among gene products. In this talk, I will discuss some recent work in my lab using machine learning techniques to predict disease comorbidity and detect missing common genes based on human interactome data. In particular, we try to incorporate biological prior knowledge when extracting interactome features and to provide insightful observations connecting characteristics of genes in the context of protein-protein interaction network to their potential roles in disease comorbidity.

Bio: Li Liao, who received a Ph.D. degree in theoretical physics from Peking University, is an associate professor of computer and information sciences at the University of Delaware. His current research is in the field of bioinformatics.
An author/co-author of more than 80 peer-reviewed publications, he is active in research and serving the bioinformatics community — he has served as a panelist for NSF, program committee member and/or organizer for over 20 conferences and workshops in bioinformatics for the past 5 years, and is currently on the editorial board of three journals. He is a senior member of the ACM.