Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2009:2009:926450.
doi: 10.1155/2009/926450. Epub 2009 Jul 20.

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

Ming-Gang Du  1 , Shan-Wen Zhang, Hong Wang
Affiliations

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

Ming-Gang Du et al. Adv Bioinformatics. 2009.

Abstract

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.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Third-order tensor.
Figure 2
Figure 2
The gene distribution frequency versus gene S-values.
Figure 3
Figure 3
Training tensor A tn.
Figure 4
Figure 4
Training core tensor S tn.
Figure 5
Figure 5
Three matrixes U 1, U 2, and U 3.
Figure 6
Figure 6
Training tensor A tn.
Figure 7
Figure 7
Training core tensor S tn.
Figure 8
Figure 8
Three matrixes U 1, U 2, and U 3.
Figure 9
Figure 9
MICA for three-order lung microarray.
See this image and copyright information in PMC

References

    1. Shalon D, Smith SJ, Brown PO. A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization. Genome Research. 1996;6(7):639–645. - PubMed
    1. Granzow M, Berrar D, Dubitzky W, Schuste A, Azuaje F, Eils R. Tumor classification by gene expression profiling: comparison and validation of five clustering methods. ACM SIGBIO Newsletter. 2001;21:16–22.
    1. Wang L, Chu F, Xie W. Accurate cancer classification using expressions of very few genes. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2007;4(1):40–53. - PubMed
    1. Alon U, Barka N, Notterman DA, et al. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proceedings of the National Academy of Sciences of the United States of America. 1999;96(12):6745–6750. - PMC - PubMed
    1. Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Haussler D. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics. 2000;16(10):906–914. - PubMed

LinkOut - more resources