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Classifier Ensemble Based Analysis of a Genome-Wide SNP Dataset Concerning Late-Onset Alzheimer Disease
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Author(s): Lúcio Coelho (Biomind LLC, USA), Ben Goertzel (Biomind LLC, USA, and Xiamen University, China), Cassio Pennachin (Biomind LLC, USA)and Chris Heward (Kronos Science Laboratory, USA)
Copyright: 2012
Pages: 10
Source title:
Breakthroughs in Software Science and Computational Intelligence
Source Author(s)/Editor(s): Yingxu Wang (University of Calgary, Canada)
DOI: 10.4018/978-1-4666-0264-9.ch025
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Abstract
In this paper, the OpenBiomind toolkit is used to apply GA, GP, and local search methods to analyze a large SNP dataset concerning Late-Onset Alzheimer’s Disease (LOAD). Classification models identifying LOAD with statistically significant accuracy are identified as well as using ensemble-based important features analysis in order to identify brain genes related to LOAD, most notably the solute carrier gene SLC6A15. Ensemble analysis is used to identify potentially significant interactions between genes in the context of LOAD.
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