Abstract
Profiling genome-wide transcriptional changes with advanced high-throughput transcriptional profiling techniques has led to a revolution in biomedical science. It has been challenging to handle the massive data generated by these techniques and draw meaningful conclusions from it. Therefore, computational biologists have developed a number of innovative methods of varying complexity and effectiveness to analyze such complex data. Over the past decade, rich information in pathway repositories has attracted and motivated researchers to incorporate such existing biological knowledge into computational analysis tools to develop what is known as pathway enrichment analysis tools. This chapter describes a new sophisticated pathway enrichment tool that exploits topology of pathway as well as expression of significantly changed genes to identify biologically significant pathways for high-dimensional gene expression datasets. Also, we demonstrate the use of this tool to analyze gene expression data from a type 2 diabetes dataset to identify a list of significantly enriched metabolic pathways.
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