By Topic

A comparative study of feature ranking methods as dimension reduction technique in Genome-Wide Association Study

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Chitra H. Ayuningtyas ; Sch. of Electr. Eng. & Inf., Inst. Teknol. Bandung, Bandung, Indonesia ; G. A. Putri Saptawati ; Tati L. E. R. Mengko

In the recent years, Genome-Wide Association Study (GWAS) has been performed by many scientist around the world to find association between genetic profiles of different individuals with the risk of developing certain diseases. GWAS are performed using the Single Nucleotide Polymorphism (SNP) data which represents the genotypes of two different groups of individuals: the case group of individuals with the disease and the control group of individuals without the disease. The very high dimensional SNP data poses challenges in analyzing GWAS result. This issue can be tackled by performing feature ranking to remove non-relevant features for reducing the dimension of the original data. This work compares several feature ranking methods including the chi-square statistics, information gain, recursive feature elimination and Relief algorithm by analyzing the performance of different learning machines combined with the feature ranking. The highest performance is gained by combining recursive feature elimination with linear SVM while the worst performance is shown by the Relief algorithm. The experiments show that the classifiers generally benefit from the feature selection, but that the highest ranked features are not the best classifier.

Published in:

Electrical Engineering and Informatics (ICEEI), 2011 International Conference on

Date of Conference:

17-19 July 2011