Cart (Loading....) | Create Account
Close category search window

Multivariate gene selection: does it help?

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
$31 $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)
Lai, C. ; Inf. & Commun. Theor. Group, Delft Univ. of Technol., Netherlands ; Reinders, M. ; Wessels, L.

When building predictors of disease state based on gene expression data, gene selection is performed in order to achieve a good performance and to identify a relevant subset of genes. Although several gene selection algorithms have been proposed, a fair comparison of the available results is very problematic. This mainly stems from two factors. First, the results are often biased, since the test set is in one way or another involved in training the predictor, resulting in optimistically biased performance estimates. Second, the published results are often based on a small number of relatively simple datasets. Therefore, no general applicative conclusions can be drawn. We therefore adopted an unbiased protocol to perform a fair comparison of state of the art multivariate and univariate gene selection techniques, in combination with a range of classifiers. Our conclusions are based on seven gene expression datasets, across many cancer types. Surprisingly, we could not detect any significant improvement of multivariate feature selection techniques over univariate approaches. We speculate on the possible causes of this finding, ranging from the small sample size problem to the particular nature of the multivariate gene dependencies.

Published in:

Computational Systems Bioinformatics Conference, 2005. Workshops and Poster Abstracts. IEEE

Date of Conference:

8-11 Aug. 2005

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.