Dr. Sumeet Dua

Max P. and Robbie L. Watson Eminent Scholar Chair

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Kameshwari Palepu (2008)

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Gene Ontology Based Gene Expression Mining; MS-CS Practicum, Student: Kameshwari Palepu (2008).

Over recent years, DNA microarrays have become the key tool in functional genomics and have become the advanced standard for understanding the underlying regulatory mechanisms of a cell.  In order to extract the genes’ biological information and to gain a better understanding of the dataset for better analysis, it is vital to incorporate external biological information about genes.  Hence, we have proposed a framework that performs the gene expression analysis by first incorporating biological knowledge and then by clustering the obtained features.The biological knowledge we incorporate is obtained from the Gene Ontology (GO) databases. A considerable amount of research and experiments have been done in the area of GO based gene expression analysis, but most of the research and experiments contain a common problem: they depend on the gene annotation’s statistics to calculate the similarity of the genes.  This method of calculation would result in variation among the biological similarity values between the genes.
The goal of our proposed study is to utilize the GO annotations together with gene expression data, in order to measure the functional similarity among the genes.  The gene expression data of yeast cells was used in our study. The Optimistic Genealogy Measure was used to measure the similarities between the GO terms, which considers both the genes’ statistics and its demographic location to calculate the genes’ functional similarity. An empirical comparison of our results with another similarity technique proposed by Wang et al. was performed in order to validate our similar matrix and to present the superiority of our similarity measure over the existing ones.  The results produced by our framework were more accurate, in that our framework grouped a set of genes showing similar biological significance into clusters.Hence, this approach can lead to more biologically meaningful clusters.

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