A support vector machine approach to identification of proteins relevant to learning in a mouse model of Down Syndrome

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Issue Date
2016-04-29
Authors
Eicher, Tara
Sinha, Kaushik
Advisor
Citation

Eicher, 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

Abstract

The 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.

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Description
Presented to the 12th Annual Symposium on Graduate Research and Scholarly Projects (GRASP) held at the Heskett Center, Wichita State University, April 29, 2016.
Research completed at Department of Electrical Engineering, College of Engineering
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