TY - GEN
T1 - Modeling oncology gene pathways network with multiple genotypes and phenotypes via a copula method
AU - Bao, Le
AU - Zhu, Zhou
AU - Ye, Jingjing
PY - 2009
Y1 - 2009
N2 - Identification of interactions between molecular features (e.g. mutation, gene expression change) and gross phenotypes in diseases and other biological processes is one of the important challenges in genomic research. Popular approaches such as GSEA are limited to hypothesis tests of bivariate association. However, a specific phenotype is often dependent upon multiple molecular features. It is thus worth considering all possible interactions jointly for a more precise and realistic representation of the cellular network. In this article, a semiparametric copula model is developed to jointly model genotypes, pathways and phenotypes to accomplish this object. A two-step procedure for reconstruction of the network is described. Simulation studies indicate that the method is effective and accurate for the network reconstruction. Application using NCI60 cancer cell line data identifies several subsets of molecular features that jointly perform as the predictors of clinical phenotypes. The copula model is expected to have a broad impact on biomedical research, ranging from cancer treatment to disease prevention.
AB - Identification of interactions between molecular features (e.g. mutation, gene expression change) and gross phenotypes in diseases and other biological processes is one of the important challenges in genomic research. Popular approaches such as GSEA are limited to hypothesis tests of bivariate association. However, a specific phenotype is often dependent upon multiple molecular features. It is thus worth considering all possible interactions jointly for a more precise and realistic representation of the cellular network. In this article, a semiparametric copula model is developed to jointly model genotypes, pathways and phenotypes to accomplish this object. A two-step procedure for reconstruction of the network is described. Simulation studies indicate that the method is effective and accurate for the network reconstruction. Application using NCI60 cancer cell line data identifies several subsets of molecular features that jointly perform as the predictors of clinical phenotypes. The copula model is expected to have a broad impact on biomedical research, ranging from cancer treatment to disease prevention.
UR - http://www.scopus.com/inward/record.url?scp=67650354595&partnerID=8YFLogxK
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U2 - 10.1109/CIBCB.2009.4925734
DO - 10.1109/CIBCB.2009.4925734
M3 - Conference contribution
AN - SCOPUS:67650354595
SN - 9781424427567
T3 - 2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2009 - Proceedings
SP - 237
EP - 246
BT - 2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2009 - Proceedings
T2 - 2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2009
Y2 - 30 March 2009 through 2 April 2009
ER -