Title

Authors

Affiliation

Faculty of Computer Science, University of Indonesia, Kampus UI Depok 16424, Indonesia

Email

ito.wasito@cs.ui.ac.id; *Corresponding author

Phone

+62 21 786 3419

Article Type

Prediction model

Date

Received May 24, 2011; Accepted June 13, 2011; Published June 23, 2011

Previous studies have been conducted in gene expression profiling to identify groups of genes that characterize the colorectal carcinoma disease. Despite the success of previous attempts to identify groups of genes in the progression of the colorectal carcinoma disease, their methods either require subjective interpretation of the number of clusters, or lack stability during different runs of the algorithms. All of which limits the usefulness of these methods. In this study, we propose an enhanced algorithm that provides stability and robustness in identifying differentially expressed genes in an expression profile analysis. Our proposed algorithm uses multiple clustering algorithms under the consensus clustering framework. The results of the experiment show that the robustness of our method provides a consistent structure of clusters, similar to the structure found in the previous study. Furthermore, our algorithm outperforms any single clustering algorithms in terms of the cluster quality score.

Citation

Wahyudi et al. Bioinformation 6(7): 279-282 (2011)
 

Edited by

P Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.