Mining Coherent Patterns and Clusters in Genomic Data

Daxin Jiang, Ph.D

Room 610, CBI, New Life Science Building, PKU
1:00-2:00 PM, Friday, 15 April,2005

Recent high-throughput biotechnologies have generated various large-scaled biological data, such as genome sequences, protein structures, gene expression measurements, protein-protein interactions and DNA binding data. These ever-increasing genomic data provide us opportunities to explore the modular organization of the cell on a genome-wide scale. As the first step toward exciting knowledge discovery, mining hidden patterns and clusters in genomic data is a critical task in bioinformatics research and biomedical applications.

In particular, microarray technology has made it possible to monitor the expression levels of thousands of genes in parallel. Many clustering algorithms have been applied to microarray data to find co-expressed genes and coherent gene expression patterns. However, due to the specific characteristics of microarray data and the special requirements from the domain of biology, clustering microarray data is still facing several challenges. In this talk, I will present three novel approaches which effectively and efficiently identify coherent expression patterns (1) across the whole experimental conditions;(2) over subsets of samples or sub-intervals of time-series; and (3) embeded in three-dimensional micorarry data, respectively. Moreover, I will introduce the problem of joint mining across multiple genomic data sets and propose a graph-based approach for mining the clusters which are consistently supported by heterogeneous data sources