ASA Connect

Topic: New Statistical and Computational Methods for Cancer Gene Discovery

Speaker: Hui Jiang, PhD, Professor of Biostatistics, University of Michigan School of Public Health

Time: Thursday, March 26, 2026, 12:00 pm – 1:30 pm E.T.

Register for free using this link: https://events.teams.microsoft.com/event/bed9de5f-c9b6-4334-9102-e76f96206aae@e51cdec9-811d-471d-bbe6-dd3d8d54c28b

Abstract: Identifying genes associated with cancer risk is a fundamental goal in cancer genomics, but statistical analysis often faces challenges such as limited sample sizes and high-dimensional genomic data. In this talk, I will present several statistical and computational methods designed to improve the discovery of cancer-associated genes. First, I will introduce an asymmetric data integration framework that leverages genomic datasets across multiple cancer types to improve power while accounting for heterogeneity among cancers. Second, I will discuss constrained variable selection methods and their applications in statistical genomics, including identifying gene pairs as potential cancer biomarkers. Efficient algorithms for fitting these models and applications to genomic data will also be discussed.

Speaker Bio: Hui Jiang is a Professor of Biostatistics in the University of Michigan School of Public Health. He received his Ph.D. in Computational and Mathematical Engineering from Stanford University and completed postdoctoral training in statistics and genomics at Stanford’s Genome Technology Center. His research focuses on developing statistical and computational methods for analyzing large-scale biological data generated by modern high-throughput technologies, including sequencing and gene expression studies. Professor Jiang’s work spans statistical genomics, bioinformatics, and high-dimensional data analysis, with applications in cancer genomics and other areas of biomedical research.

Host and Contact: Yang Shi, Karmanos Cancer Institute (yangsh (AT) karmanos.org)