Predicting disease phenotypes based on the molecular networks with Condition-Responsive Correlation

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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.
Publisher
INDERSCIENCE ENTERPRISES LTD
Issue Date
2011-03
Language
English
Article Type
Article
Keywords

PROTEIN INTERACTION NETWORK; BREAST-CANCER; CLASSIFICATION; DISCOVERY; PROSTATE; DATABASE; METASTASIS; RESOURCE; MOTILITY; INVASION

Citation

INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS, v.5, no.2, pp.131 - 142

ISSN
1748-5673
URI
http://hdl.handle.net/10203/99734
Appears in Collection
BiS-Journal Papers(저널논문)
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