Please use this identifier to cite or link to this item: http://drsr.daiict.ac.in//handle/123456789/955
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dc.contributor.advisorMajumder, Prasenjit
dc.contributor.authorDave, Bhargav Deven
dc.date.accessioned2020-02-22T05:04:15Z
dc.date.available2023-02-17T05:04:15Z
dc.date.issued2020
dc.identifier.citationDave, Bhargav Deven (2020). Study of microarray data to identify putative risk genes in Parkinson’s disease. Dhirubhai Ambani Institute of Information and Communication Technology. viii, 61 p. (Acc.No: T00877)
dc.identifier.urihttp://drsr.daiict.ac.in//handle/123456789/955
dc.description.abstractThis study focussed on identification of risk genes involved in PD.We used three methods namely WGCNA, DEGs Analysis and PPI to identify such important downregulated and upregulated genes. The three methods show high agreement with each other and also with the available biomedical literature on this neurodegenerative disease. In this study, 20 significantly (p-value) upregulated and 19 significantly (p-value) downregulated genes have been found to have notable contribution in the manifestation of motor and non motor symptoms of the disease as interpreted from the enrichment analysis. Gene expression dataset of Parkinson’s Disease (PD) obtained from the Gene Expression Omnibus (GEO), a free public functional genomics data repository of an array and sequence-based data. In this study, three gene expression profiles/datasets GSE8397, GSE20164, and GSE20295 are used, which are obtained using Affymetrix Human Genome U133A Array chip. This study shows a promise in identifying potential biomarkers that would enable in the early diagnosis of Parkinson’s Disease prior to the onset of disease symptoms.
dc.publisherDhirubhai Ambani Institute of Information and Communication Technology
dc.subjectMicroarray
dc.subjectParkinson’s disease
dc.subjectDEGs
dc.subjectWGCNA
dc.subjectPPIs
dc.classification.ddc572.8 DAV
dc.titleStudy of microarray data to identify putative risk genes in Parkinson’s disease
dc.typeDissertation
dc.degreeM. Tech
dc.student.id201811049
dc.accession.numberT00877
Appears in Collections:M Tech Dissertations

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