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dc.contributor.authorSharma, Ashok Kumar
dc.date.accessioned2021-05-02T17:54:15Z
dc.date.available2021-05-02T17:54:15Z
dc.date.issued2011
dc.identifier.urien
dc.identifier.urihttp://hdl.handle.net/10675.2/623986
dc.description.abstractRecent high throughput technologies such as microarrays have made it possible to accurately and efficiently monitor transcription levels of thousands of genes in parallel. Genomic data generated by these microarray experiments offer tremendous potential for advances in molecular biology and functional genomics. With the exploding volume of genomics data, the analysis and integration of datasets to extract biological meaning becomes challenging. Cluster analysis is typically the first step in expression data analysis and knowledge discovery. Many cluster analysis algorithms have been developed to analyze gene expression microarray data. However, due to high computational cost, most algorithms are not time effective and fail when clustering larger datasets. To overcome this limitation, we have developed a new memory efficient clustering algorithm which performs clustering of extremely large datasets in ' minimal time. We have implemented this algorithm as a software tool (HPCiuster), which is designed to provide the research community with an easy and manageable client-server Windows application. Another key analysis for extracting biological information from genomics data is the elicitation of gene interaction networks underlying complex diseases. MicroRNAs add another complex layer to these gene regulatory networks. Recently, microRNAs have been found to be key regulators of gene expression and are involved in many biological processes. However, the impact of microRNA mediated gene regulation in complex diseases is largely unknown. This dissertation has made an attempt to develop a strategy to combine the gene expression data and microRNA expression data to generate microRNA regulatory networks that might be involved in the changes observed in the gene expression. We developed new tools to streamline the process while incorporating several publicly available bioinformatics resources. As a proof of principle, we apply this approach to identify the molecular mechanisms of leptin mediated weight loss in a systems-oriented manner. We found several genes, microRNAs, subnetworks, pathways, biological processes, microRNA-mRNA modules that respond to leptin treatment and might be involved in leptinmediated weight loss in ob/ob mice. These findings will help researchers in hypothesis generation to carry out further studies to find the precise molecular mechanisms of leptin action and for the treatment of obesity.en_US
dc.language.isoen_USen_US
dc.publisherAugusta Universityen_US
dc.rightsCopyright protected. Unauthorized reproduction or use beyond the exceptions granted by the Fair Use clause of U.S. Copyright law may violate federal law.en_US
dc.subjectWeight Lossen_US
dc.subjectGene Expressionen_US
dc.titleAn Integrative genomics approach identifies key pathways and microRNA networks associated with leptin-mediated weight lossen_US
dc.typeDissertationen_US
dc.contributor.departmentMedical College of Georgiaen_US
dc.description.advisorN/A, N/A
dc.description.committeeN/A, N/A
dc.description.degreeDoctor of Philosophyen_US
dc.embargoen
refterms.dateFOA2021-05-02T17:54:16Z


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