Mining localized co-expressed gene patterns from microarray data

Abstract

Analyzing the microarray data to identify localized co-expressed gene patterns are essential in revealing the gene functions, gene regulations, subtypes of cells, and cellular processes of gene regulation networks. Hence, researchers are recently motivated to mine co-expressed gene patterns from microarray data. This thesis studies both the static and dynamic aspects of localized co-expressed gene patterns and categories the patterns into three types: co-attribute patterns, co-tendency patterns and time-lagged patterns. Designing new algorithms to identify the three types of localized co-expressed gene patterns is the research problem of this thesis. We present in this thesis a series of new algorithms to mine localized co-expressed gene patterns. We conduct experiments on both synthetic and real microarray datasets. Our experiments show the effectiveness and efficiency of our algorithms in mining the localized co-expressed gene patterns. We believe our research in this thesis delivers valuable information and provides excellent tools for bioinformatics research.