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Using Frequent Co-expression Network to Identify Gene Clusters for Breast Cancer Prognosis

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4 Author(s)
Jie Zhang ; Dept. of Biomed. Inf., Ohio State Univ., Columbus, OH, USA ; Kun Huang ; Yang Xiang ; Ruoming Jin

In this paper, we investigated the use of gene coexpression network analyses to identify potential biomarkers for breast carcinoma prognosis. The network mining algorithm CODENSE is used to identify highly connected genome-wide gene co-expression networks among a variety of cancer types, and the resulted gene clusters are applied to a series of breast cancer microarray sets to categorize the patients into different groups. As a result, we have identified a set of genes that are potential biomarkers for breast cancer prognosis which can categorize the patients into two groups with distinct prognosis. We also compared the gene clusters we discovered with gene subsets identified from similar studies using other clustering algorithms.

Published in:

Bioinformatics, Systems Biology and Intelligent Computing, 2009. IJCBS '09. International Joint Conference on

Date of Conference:

3-5 Aug. 2009