Clustering-based gene-subnetwork biomarker identification using gene expression data
Conference proceedings article
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Publication Details
Author list: Doungpan N., Engchuan W., Meechai A., Chan J.H.
Publisher: Hindawi
Publication year: 2015
Volume number: 2015-September
ISBN: 9781479919604; 9781479919604; 9781479919604; 9781479919604
ISSN: 0146-9428
eISSN: 1745-4557
Languages: English-Great Britain (EN-GB)
Abstract
The identification of predictive biomarkers of complex disease with robustness and specificity is an ongoing challenge. Gene expressions provide information on how the cell reacts to a particular state and the relationship of genes may lead to novel information. A network-based approach integrating expression data with protein-protein interaction network can be used to identify gene-subnetwork biomarkers for a particular disease. However, cancer datasets are heterogeneous in nature containing unknown or undefined subtypes of cancers. In this study, we propose a gene-subnetwork biomarker identification approach by implementing an Expectation-Maximization (EM) clustering technique to homogenize the dataset. To validate our proposed method. Lung cancer expression datasets are used to identify gene-subnetwork biomarkers. The evaluation of gene-subnetwork biomarkers is done by 5-fold cross-validation on an independent dataset. The comparison between non-clustering and clustering-based gene-subnetwork identification showed that clustering produced improved classification performance at a statistically significant level. Furthermore, preliminary functional analysis results showed more significant subnetworks were identified using the proposed approach. ฉ 2015 IEEE.
Keywords
gene-subnetwork
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