Journal article

Publication year: 2011

Pages: 1645-1652

Journal: Bioinformatics

Volume number: 27

Issue number: 12

ISSN: 1367-4803

eISSN: 1460-2059

DOI Link: https://doi.org/10.1093/bioinformatics/btr199

Publisher: Oxford University Press

Abstract: 
Motivation: Changes in gene expression levels play a central role in tumors. Additional information about the distribution of gene expression levels and distances between adjacent genes on chromosomes should be integrated into the analysis of tumor expression profiles.Results: We use a Hidden Markov Model with distance-scaled transition matrices (DSHMM) to incorporate chromosomal distances of adjacent genes on chromosomes into the identification of differentially expressed genes in breast cancer. We train the DSHMM by integrating prior knowledge about potential distributions of expression levels of differentially expressed and unchanged genes in tumor. We find that especially the combination of these data and to a lesser extent the modeling of distances between adjacent genes contribute to a substantial improvement of the identification of differentially expressed genes in comparison to other existing methods. This performance benefit is also supported by the identification of genes well known to be associated with breast cancer. That suggests applications of DSHMMs for screening of other tumor expression profiles.

Harvard Citation style: Seifert, M., Strickert, M., Schliep, A. and Grosse, I. (2011) Exploiting prior knowledge and gene distances in the analysis of tumor expression profiles with extended Hidden Markov Models, Bioinformatics, 27(12), pp. 1645-1652. https://doi.org/10.1093/bioinformatics/btr199

APA Citation style: Seifert, M., Strickert, M., Schliep, A., & Grosse, I. (2011). Exploiting prior knowledge and gene distances in the analysis of tumor expression profiles with extended Hidden Markov Models. Bioinformatics. 27(12), 1645-1652. https://doi.org/10.1093/bioinformatics/btr199