Classifier ensemble based analysis of a genome-wide SNP dataset concerning Late-Onset Alzheimer Disease

Author

Coelho, Lúcio ; Goertzel, Ben ; Pennachin, Cassio ; Heward, Chris

Author_Institution

Biomind LLC, Rockville, MD, USA

fYear

2009

fDate

15-17 June 2009

Firstpage

469

Lastpage

475

Abstract

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, and ensemble-based important features analysis is used 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.

Keywords

brain; diseases; genetic algorithms; genetic engineering; learning (artificial intelligence); medical administrative data processing; search problems; OpenBiomind toolkit; SLC6A15; brain genes; classifier ensemble based analysis; genetic algorithms; genetic programming; genome-wide SNP dataset; important features analysis; late-onset Alzheimer disease; local search methods; single-nucleotide polymorphisms; Algorithm design and analysis; Alzheimer´s disease; Bioinformatics; Biological information theory; Data analysis; Genetic programming; Genomics; Machine learning; Search methods; Testing; Alzheimer; GA; GP; SNP;

fLanguage

English

Publisher

ieee

Conference_Titel

Cognitive Informatics, 2009. ICCI '09. 8th IEEE International Conference on

Conference_Location

Kowloon, Hong Kong

Print_ISBN

978-1-4244-4642-1

Type

conf

DOI

10.1109/COGINF.2009.5250695

Filename

5250695