Type 2 Diabetes Mellitus (T2DM) being a complex metabolic disease is recognized as one of the potential threat to the human health in the 21st century. Etiologically it is characterized by insulin resistance and diminished insulin secretion. Advances in gene-expression studies related to T2DM have revealed altered expression of a large number of metabolic genes in a variety of tissues. Through a cluster based analysis of microarray datasets, we have identified altered genes associated with insulin signaling. We have also elucidated the application of self-organizing maps (SOMs); a type of mathematical cluster analysis technique that is pertinent for the recognition and classification features in a complex multidimensional gene-expression data. In order to investigate T2DM related alterations in expression of influenced Insulin signaling genes and transcription factors, we have implemented a network-centric methodology. It is also analyzed that these gene-sets share one or more transcription factor binding sites in the promoter regions of the corresponding genes enabling the determination of regulatory mechanisms that lead to gene expression changes in gene network. Furthermore, Gene Set Enrichment Analysis (GSEA) was used to interpret gene expression data to find gene sets sharing common biological function and regulation. Finally, we calculated gene evolutionary rate to explore the lineage distribution amongst all insulin signaling genes.

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Gene Expression K-means Clustering Principal Component Analysis Self-organizing Maps Type 2 Diabetes Mellitus