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dc.contributor.authorEicher, Tara
dc.contributor.authorSinha, Kaushik
dc.date.accessioned2016-07-06T14:58:47Z
dc.date.available2016-07-06T14:58:47Z
dc.date.issued2016-04-29
dc.identifier.citationEicher, Tara, & Sinha, Kaushik. 2016. A support vector machine approach to identification of proteins relevant to learning in a mouse model of Down Syndrome. --In Proceedings: 12th Annual Symposium on Graduate Research and Scholarly Projects. Wichita, KS: Wichita State University, p. 43
dc.identifier.urihttp://hdl.handle.net/10057/12190
dc.descriptionPresented to the 12th Annual Symposium on Graduate Research and Scholarly Projects (GRASP) held at the Heskett Center, Wichita State University, April 29, 2016.
dc.descriptionResearch completed at Department of Electrical Engineering, College of Engineering
dc.description.abstractThe drug memantine has been shown to improve learning ability in a Down Syndrome model of mice (Ts65Dn) exposed to Context Fear Conditioning (CFC), an existing technique for measuring the learning ability of normal mice which does not typically produce results in Ts65Dn mice. This work seeks to increase the understanding of how memantine affects the learning ability of Ts65Dn mice at the protein level by analyzing the expression of 77 proteins obtained from the brains of normal and Ts65Dn mice, with and without memantine and with and without exposure to CFC. Support Vector Machines are used for pairwise classification of the groups of mice based on protein expression. Feature selection is then used to choose the proteins whose levels appear to be significant for each classification. The majority of classifiers outperform previous analysis methods in terms of prediction accuracy, producing a reliable subset of proteins for further biological study.
dc.description.sponsorshipGraduate School, Academic Affairs, University Libraries, Regional Institute on Aging
dc.language.isoen_US
dc.publisherWichita State University
dc.relation.ispartofseriesGRASP
dc.relation.ispartofseriesv. 12
dc.titleA support vector machine approach to identification of proteins relevant to learning in a mouse model of Down Syndrome
dc.typeAbstract
dc.rights.holderWichita State University


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