Single nucleotide polymorphism selection using independent component analysis.

Bioinformatics research in genome wide association studies necessitates the development of algorithms capable of manipulating very-large datasets of Single Nucleotide Polymorphisms (SNP). To facilitate such association studies, we propose a novel framework for SNP selection using Independent Component Analysis (ICA). Compared to previous ICA-based methods, our framework works as a filtering technique to reduce the number of SNPs in a dataset, without the need for any class labels.

Fast orthogonal search for genetic feature selection.

In this paper, we present the application of a multivariate regression approach, fast orthogonal search, to select the most informative features in Single Nucleotide Polymorphism data, and to use these features to accurately model the entire data. Our results on two published datasets show very high accuracies in capturing the hidden information in the sequence of studied SNPs. The execution time for our developed methodology is very short and paves the way for its application to large-scale genome wide datasets.