GRAND Seminar: Some Expeditions in Predictive Modeling to Enable Systems

Friday, March 28, 2014
ENGR 4801
*Gaurav Pandey*

With the data explosion being witnessed in biology, immense emphasis is
being placed on developing systematic approaches to integrate the various
types and sources of data to build models of complex biological processes
and diseases.

In this talk, I will discuss our efforts to model complex biomedical
phenotypes using predictive modeling approaches applied to large
genome-wide data sets. The first part presents experiences and results from
a collaborative-competitive effort to model and predict survival rates for
breast cancer patients using a recently published set of gene expression,
copy number aberration and clinical features.

In the second part, I will present our analysis of heterogeneous ensemble
predictive methods that generally produce the best performance for complex
biomedical prediction problems. These methods leverage the consensus and
diversity among hundreds or even thousands of heterogeneous base
predictors, and thus generally outperform even the best homogeneous
ensemble methods, like boosting and random forests.
Speaker's BioGaurav Pandey is an Assistant Professor in the Department of
Genetics and Genomic Sciences at the Mount Sinai School of Medicine (New
York) and is part of the newly formed Institute for Genomics and Multiscale
Biology. He completed his Ph.D. in computer science and engineering from
the University of Minnesota, Twin Cities in 2010, and subsequently
completed a post-doctoral fellowship at the University of California,
Berkeley. His primary fields of interest are computational biology,
genomics and large-scale data analysis and mining, and he has published
extensively in these areas.