Modeling oncology gene pathways network with multiple genotypes and phenotypes via a copula method

@article{Bao2009ModelingOG,
  title={Modeling oncology gene pathways network with multiple genotypes and phenotypes via a copula method},
  author={Le Bao and Zhou Zhu and Jingjing Ye},
  journal={2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology},
  year={2009},
  pages={237-246}
}
  • Le Bao, Zhou Zhu, Jingjing Ye
  • Published 30 March 2009
  • Biology
  • 2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology
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… 
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References

SHOWING 1-10 OF 42 REFERENCES
A Markov random field model for network-based analysis of genomic data
TLDR
A Markov random field (MRF)-based model efficiently utilizes the known pathway structures in identifying the DE genes and the subnetworks that might be related to phenotype.
A Copula Approach for Detecting Prognostic Genes Associated With Survival Outcome in Microarray Studies
TLDR
The proposed method addresses the discovery of a relatively small panel of genes whose elements are associated with a relevant clinical outcome variable such as time‐to‐death or time-to‐recurrence of disease by direct incorporation of the censoring mechanism and by appropriate statistical adjustment for multiplicity.
Discovering statistically significant pathways in expression profiling studies.
TLDR
A statistical framework for determining whether a specified group of genes for a pathway has a coordinated association with a phenotype of interest is proposed, and it is shown that the differences in the correlation structure of each set of genes can lead to a biased comparison among gene sets unless a normalization procedure is applied.
Network-constrained regularization and variable selection for analysis of genomic data
TLDR
A network-constrained regularization procedure that efficiently utilizes the known pathway structures in identifying the relevant genes and the subnetworks that might be related to phenotype in a general regression framework is introduced.
Gene Copy Number Analysis for Family Data Using Semiparametric Copula Model
TLDR
A semiparametric model based on clustering method in which the marginal distributions are estimated nonparametrically, and the familial dependence structure is modeled by copula is considered, showing that the proposed method is more robust than the commonly used multivariate normal model.
A copula method for modeling directional dependence of genes
TLDR
This method is able to overcome the limitation of Bayesian network method for gene-gene interaction, i.e. information loss due to binary transformation, and can be an alternative to Bayesian networks in modeling gene interactions.
Oncogenic pathway signatures in human cancers as a guide to targeted therapies
TLDR
It is shown that gene expression signatures can be identified that reflect the activation status of several oncogenic pathways and linked with sensitivity to therapeutics that target components of the pathway provides an opportunity to make use of these oncogens pathway signatures to guide the use of targeted therapeutics.
Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
TLDR
It is demonstrated how the GSEA method yields insights into several cancer-related data sets, including leukemia and lung cancer, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer.
EXAMINE: a computational approach to reconstructing gene regulatory networks.
Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks
TLDR
It is shown that PCIT is more sensitive than established methods and capable of detecting functionally validated gene-gene interactions coming from absolute r values as low as 0.3.
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