Table of Contents Author Guidelines Submit a Manuscript
Advances in Bioinformatics
Volume 2009 (2009), Article ID 926450, 9 pages
Research Article

Tumor Classification Using High-Order Gene Expression Profiles Based on Multilinear ICA

1School of Urban and Environment Science, Shanxi Normal University, Linfen, Shanxi 041004, China
2Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui 230031, China
3College of Mathematics and Computer Science, Shanxi Normal University, Linfen, Shanxi 041004, China

Received 8 February 2009; Revised 8 May 2009; Accepted 14 May 2009

Academic Editor: Satoru Miyano

Copyright © 2009 Ming-gang Du et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Motivation. Independent Components Analysis (ICA) maximizes the statistical independence of the representational components of a training gene expression profiles (GEP) ensemble, but it cannot distinguish relations between the different factors, or different modes, and it is not available to high-order GEP Data Mining. In order to generalize ICA, we introduce Multilinear-ICA and apply it to tumor classification using high order GEP. Firstly, we introduce the basis conceptions and operations of tensor and recommend Support Vector Machine (SVM) classifier and Multilinear-ICA. Secondly, the higher score genes of original high order GEP are selected by using t-statistics and tabulate tensors. Thirdly, the tensors are performed by Multilinear-ICA. Finally, the SVM is used to classify the tumor subtypes. Results. To show the validity of the proposed method, we apply it to tumor classification using high order GEP. Though we only use three datasets, the experimental results show that the method is effective and feasible. Through this survey, we hope to gain some insight into the problem of high order GEP tumor classification, in aid of further developing more effective tumor classification algorithms.