Dr. Sumeet Dua

Max P. and Robbie L. Watson Eminent Scholar Chair

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Vijay Raj Kukkala (2005)

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Relevant Feature Extraction Using Gene Ontology for Cancer Classification; MS-CS Thesis; Student: Vijay Raj Kukkala (2005)

Microarray gene expression data analysis has been one of the major research areas in the field of Bioinformatics. Among the several uses of microarray data, the functional categorization of genes and the classification of cancer samples into classes for early diagnosis have been of much interest. In our thesis, we have addressed the latter issue.  Many methods like hierarchical clustering, self organizing maps, and support vector machines have been published in literature, which try to identify genes useful for classification using mathematical models. However, not many methods delve into categorization through functional aspects. The motivation behind our work is to explore and use the functional relationship in addition to the mathematical relationship of the genes, in classifying disease specific samples.
We propose to find functional relationships between genes using the Gene Ontology hierarchy. After preprocessing the expression data, we use the apriori algorithm to find association rules, which reflect all possible gene-pair relationships using some criterion.  This obtained set of gene pairs is examined for similarity using the Gene Ontology hierarchy for functional relationship. Obtained functionally, similar genes are used in the unsupervised classification of the samples. Using our relevant feature extraction method, we have been able to classify more than 82% of the samples accurately.

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