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dc.contributor.authorArreola, Ivan
dc.contributor.authorHan, David
dc.date.accessioned2020-06-08T22:29:41Z
dc.date.available2020-06-08T22:29:41Z
dc.date.issued2018
dc.identifier.issn2470-3958
dc.identifier.urihttps://hdl.handle.net/20.500.12588/61
dc.description.abstractMicroarray analysis can help identify changes in gene expression which are characteristic to human diseases. Although genomewide RNA expression analysis has become a common tool in biomedical research, it still remains a major challenge to gain biological insight from such information. Gene Set Analysis (GSA) is an analytical method to understand the gene expression data and extract biological insight by focusing on sets of genes that share biological function, chromosomal regulation or location. Thing systematic mining of different gene-set collections could be useful for discovering potential interesting gene-sets for further investigation. Here, we seek to improve previously proposed GSA methods for detecting statistically significant gene sets via various score transformations.en_US
dc.language.isoen_USen_US
dc.publisherOffice of the Vice President for Researchen_US
dc.relation.ispartofseriesThe UTSA Journal of Undergraduate Research and Scholarly Work;Volume 4
dc.subjectGene Expressionen_US
dc.subjectGene Set Analysisen_US
dc.subjectGene Set Enrichment Analysisen_US
dc.subjectGenomicsen_US
dc.subjectMicro-array Analysisen_US
dc.titleComparison of Gene Set Analysis with Various Score Transformations to Test the Significance of Sets of Genesen_US
dc.typeArticleen_US
dc.description.departmentManagement Science and Statistics


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