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

ABSTRACT: In this paper, we investigated the use of gene co-expression 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.

SUBMITTER: Zhang J

PROVIDER: S-EPMC3632312 | biostudies-literature | 2009 Aug

REPOSITORIES: biostudies-literature

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

Zhang Jie J Huang Kun K Xiang Yang Y Jin Ruoming R

Proceedings ... International Joint Conference on Bioinformatics, Systems Biology and Intellgent Computing. International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing 20090801

In this paper, we investigated the use of gene co-expression 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 biomar ...[more]

PMID: 23615925

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