New Statistical and Computational Methods for Cancer Gene Discovery
This event is in the past.
Please join us on Thursday, March 26, 2026, at 12:00pm-1:30pm for the Biostatistics & Bioinformatics Core's Webinar. Hui Jiang, PhD, Professor of Biostatistics at the Univeristy of Michigan School of Public Health, will be giving a presentation on, "New Statistical and Computational Methods for Cancer Gene Discovery" This is a virtual only seminar. Registration is required. You may register for free using the link below.
Register for free using the following link:
*Upon registration, you will receive a confirmation email with information about joining the meeting.
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.