Fast orthogonal search for genetic feature selection

Author

Nahlawi, Layan Imad ; Mousavi, Parvin

Author_Institution

Sch. of Comput., Queen´´s Univ., Kingston, ON, Canada

fYear

2010

fDate

Aug. 31 2010-Sept. 4 2010

Firstpage

1077

Lastpage

1080

Abstract

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.

Keywords

bioinformatics; genetics; genomics; polymorphism; regression analysis; search problems; fast orthogonal search; genetic feature selection; large-scale genome wide dataset; multivariate regression approach; single nucleotide polymorphism; Accuracy; Biological cells; Cutoff frequency; Data models; Genetics; Histograms; Matrix decomposition; Algorithms; Base Sequence; DNA; DNA Mutational Analysis; Humans; Molecular Sequence Data; Pattern Recognition, Automated; Polymorphism, Single Nucleotide; Sequence Analysis, DNA;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE

Conference_Location

Buenos Aires

ISSN

1557-170X

Print_ISBN

978-1-4244-4123-5

Type

conf

DOI

10.1109/IEMBS.2010.5627300

Filename

5627300