Dissemin is shutting down on January 1st, 2025

Published in

Future Medicine, BioTechniques, 5(45), p. 501-520, 2008

DOI: 10.2144/000112950

Links

Tools

Export citation

Search in Google Scholar

A review of independent component analysis application to microarray gene expression data

Journal article published in 2008 by Wei Kong, Charles R. Vanderburg ORCID, Hiromi Gunshin, Jack T. Rogers, Xudong Huang
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

Full text: Download

Green circle
Preprint: archiving allowed
Upload
Green circle
Postprint: archiving allowed
Upload
Green circle
Published version: archiving allowed
Upload
Policy details
Data provided by SHERPA/RoMEO

Abstract

Independent component analysis (ICA) methods have received growing attention as effective data-mining tools for microarray gene expression data. As a technique of higher-order statistical analysis, ICA is capable of extracting biologically relevant gene expression features from microarray data. Herein we have reviewed the latest applications and the extended algorithms of ICA in gene clustering, classification, and identification. The theoretical frameworks of ICA have been described to further illustrate its feature extraction function in microarray data analysis.