There are a variety of challenges in the analysis of multi-omic data in molecular biology, including issues of sparsity in data sets and statistical significance across a wide variety of features.  We will review these problems in the context of a sample dataset, briefly discussing best practices related to multiple comparisons, false discovery rate, and supervised and unsupervised learning techniques. We will also discuss current state-of-the-art techniques for inferring networks of gene regulatory interactions from multi-omic data.

Place: Hybrid: Math, 402/Zoom:   Link  https://arizona.zoom.us/j/83541348598  Password:  BB2022