Title: Predicting disease phenotypes based on the molecular networks with Condition-Responsive Correlation
Authors: Sejoon Lee, Eunjung Lee, Kwang H. Lee, Doheon Lee
Addresses: Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea. ' Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA. ' Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea. ' Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea
Abstract: Network-based methods using molecular interaction networks integrated with gene expression profiles have been proposed to solve problems, which arose from smaller number of samples compared with the large number of predictors. However, previous network-based methods, which have focused only on expression levels of proteins, nodes in the network through the identification of condition-responsive interactions. We propose a novel network-based classification, which focuses on both nodes with discriminative expression levels and edges with Condition-Responsive Correlations (CRCs) across two phenotypes. We found that modules with condition-responsive interactions provide candidate molecular models for diseases and show improved performances compared conventional gene-centric classification methods.
Keywords: molecular models; CRC; condition-responsive correlation; phenotype classification; network-based classification; molecular interaction networks; gene expression profiles; disease phenotypes; bioinformatics.
International Journal of Data Mining and Bioinformatics, 2011 Vol.5 No.2, pp.131 - 142
Published online: 24 Mar 2011 *Full-text access for editors Access for subscribers Purchase this article Comment on this article