|Title||:||Computational dissection of gene networks in health and disease|
|Speaker||:||Manikantan Narayanan (National Institute of Health, USA)|
|Details||:||Fri, 22 Apr, 2016 11:00 AM @ BSB 361|
|Abstract:||:||High-dimensional datasets are routinely generated in life sciences (protein interaction, gene expression, and DNA/RNA sequence data, to name a few), stretching our ability to derive novel insights from them, with even less effort focused on integrating disparate data available in the public domain. Hence a most pressing problem in life sciences today is the development of algorithms to combine large-scale data on different biological dimensions and different resolutions from single cells to composite tissues. The natural computational problem in these settings is to find sets of nodes (genes) that simultaneously form well-connected clusters in diverse networks (defined using different edge sets over the same set of nodes). In this talk, I intend to present graph algorithms for this problem using different quality measures like connectivity and conductance, and show how certain biologically-inspired matching criteria offers tractable alternatives to previous NP-hard graph-matching problem formulations. These generic algorithms could have applications beyond biology in other data science fields as well.
Bio: Manikandan Narayanan is a Staff Research Scientist in the Laboratory of Systems Biology at the National Institutes of Health (NIH). He enjoys working on computational models and algorithms that strive towards an integrated view of life from molecules to whole organisms. He obtained his Ph.D. in Computer Science (with an emphasis in computational and genomic biology) from the University of California at Berkeley, and was a Sr. Research Scientist at Merck Research Labs in Seattle and Boston before arriving at the NIH. Manikandan is a Siebel Scholar Class of 2003, and has authored publications in top-ranking computational biology journals. He is intrigued by the application of data science in areas beyond biology such as climate-conscious city planning.