ECE Graduate Seminar
<p> <strong>Mining the Mouse Brain Data: Clustering, Prediction, and Networks</strong></p> <p> <strong>By</strong></p> <p> <strong>Dr. Shuiwang Ji </strong></p> <p> <strong>Department of Computer Science</strong></p> <p> <strong>Old Dominion University</strong></p> <p> <strong>Host: Dr. Oscar González </strong></p> <p> <strong>Friday, March 8, 2013</strong></p> <p> <strong>3:00 p.m. KH 224</strong></p> <p> The mammalian brain controls cognition, emotion, and perception and is one of the most complex yet least understood biological systems. It is known that there are at least several hundreds of distinct types of cells in the mammalian brain. These cell types are arranged into complex circuits, which ultimately are responsible for generating brain function. The phenotypic properties of cells of different types are largely the consequences of unique combinations of expressed gene products; therefore, analysis of gene expression patterns provides an informative modality to study functional brain circuitry.</p> <p> In this talk, I will present our work on mining the gene expression and brain connectivity data from the Allen Brain Atlas (ABA), which provides genome-scale, cellular-resolution, three-dimensional gene expression and connectivity patterns in the mouse brain. I will discuss how unsupervised learning of the spatiotemporal gene expression data can lead to results that are consistent with classical neuroanatomy. I will then present our work on correlating and predicting the brain connectivity using gene expression signatures. Finally, I will describe our work on modeling the brain networks.</p> <p> </p>
Posted By: Linda Marshall
Date: Mon Mar 04 11:22:34 EST 2013