By Topic

Applying gene ontology to microarray gene expression data analysis

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)
Andy C. Yang ; Department of CSIE, Tamkang University, TKU, Taipei, Taiwan, R.O.C. ; Hui-Huang Hsu ; Ming-Da Lu

Selecting informative genes from microarray gene expression data is the most important task while performing data analysis on the large amount of data. Mining genes having regulatory relations within thousands of genes is essential. To fit this need, a number of methods were proposed from various points of view. However, most existing methods solely focus on gene expression values themselves without using any external information of genes. Gene Ontology (GO) provides biological information of genes or proteins involved. It utilizes a hierarchical structure to give additional biological information of genes as the aid for data analysis. In this paper, we first give a brief description about the GO structure and give a review of existing literatures that take GO into account. Subsequently, we propose a novel method to identify regulatory gene pairs in a real microarray dataset based on dynamic time warping (DTW) algorithm and GO. Finally, we summarize this paper with a discussion on how GO can be used to facilitate the analysis of microarray gene expression data.

Published in:

2010 International Conference on System Science and Engineering

Date of Conference:

1-3 July 2010