Biostatistics & Medical Informatics seminar on Friday October 15th

Please join us for next week’s Biostatistics & Medical Informatics seminar on Friday!

Time: Friday, October 15 2021 at 12:00-1:00pm

Zoom Link:

Please note that this seminar is fully virtual.

Title: Machine learning as an assay for high-dimensional biology

Presenter: Dr. Sara Mostafavi, School of Computer Science and Engineering, University of Washington

Abstract: The growing availability of hundreds of different functional genomic assays across thousands of individuals, presents an exciting opportunity to understand the inner workings of biological systems, so to identify molecular causes of disease. Toward this goal, machine learning (ML) provides a powerful set of tools to integrate diverse datasets, uncovering hidden structure that can reveal how different layers of biological systems relate to each other. However, to harness the power of ML for biology, we need to be able to tune it so to distinguish meaningful structure from those that arise because of artifact and noise. In this talk, I’ll present machine learning approaches recently developed by my lab for leveraging heterogeneous data and prior knowledge to guide discovery of meaningful biological structure. In particular, I will first describe our deep learning approach (AI-TAC) to combining a large compendium of epigenomic data, in order to learn the relationship between non-coding sequence and regulatory activity across the immune system. I will describe how we robustly interrogate this model to gain mechanistic insights into the non-coding genome for implementing cell-type specific gene regulation. I will then focus on the challenging task of understanding molecular causes of complex disease. Here, I will describe our robust ML techniques for revealing mechanistic insights into Alzheimer’s disease by combining large and heterogeneous gene expression datasets from the brain. Together, these examples illustrate that ML, and in particular deep learning, is an important “assay” that can synthesize information across multiple types of experimental assays to yield new insights into gene regulation and disease.