CS Colloquium 4/20/2012 "Mining High-Throughput Biological Data"
Mining High-Throughput Biological Data
Dr. Xiang Zhang
Assistant Professor Electric Engineering and Computer Science Department
Case Western Reserve University
3:45 P.M. - 5:00 P.M., April 20th 2012, MSB 228
Advanced biotechnologies have rendered feasible high-throughput data collecting in human and other model organisms. The availability of such data holds promise for dissecting complex biological processes. Making sense of the flood of biological data poses greatstatistical and computational challenges.
In this talk, I will discuss the problem of finding gene-gene interactions in high-throughput genetic data. Finding genetic interactions is an important biological problem since many common diseases are caused by joint effects of genes. Previously, it was considered intractable to find genetic interactions in the whole-genome scale due to the enormous search space. The problem was commonly addressed using heuristics which do not guarantee the optimality of the solution. I will show that by utilizing the upper bound of the test statistic and effectively indexing the data, we can dramatically prune the search space and reduce computational burden. Moreover,our algorithms guarantee to find the optimal solution. In addition to handling specific statistical tests, our algorithms can be applied to a wide range of study types by utilizing convexity, a common property of many commonly used statistics.
Xiang Zhang is an assistant professor in the Electric Engineering and Computer Science Department at Case Western Reserve University. He received his Ph.D. from the Department of Computer Science at the University of North Carolina at Chapel Hill in 2011. His research focuses on data mining and bioinformatics. Working closely with biologists and statisticians, he has developed effective and efficient techniques for analyzing high-throughput genetic and genomic data. For his research, he has won a best student paper award at IEEE ICDE 2008 and a best research paper award at ACM SIGKDD 2008. He is a recipient of a Microsoft Research Ph.D. Fellowship in 2009-2011.