ArticlePLoS computational biology2006
Inference of disease-related molecular logic from systems-based microarray analysis.
Article in PLoS computational biology, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Differentially Coexpressed Disease Gene Identification Based on Gene Coexpression Network.BioMed research international · 2016Pooled it
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- Discovering Pair-wise Synergies in Microarray Data.Scientific reports · 2016Article
- An Information Theoretical Study of the Epistasis Between the CNR1 1359 G/A Polymorphism and the Taq1A and Taq1B DRD2 Polymorphisms: Assessing the Susceptibility to Cannabis Addiction in a Turkish Population.Journal of molecular neuroscience : MN · 2016Article
- An information theoretic method to identify combinations of genomic alterations that promote glioblastoma.Journal of molecular cell biology · 2015Article
- Gene differential coexpression analysis based on biweight correlation and maximum clique.BMC bioinformatics · 2014Article
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- High-order SNP combinations associated with complex diseases: efficient discovery, statistical power and functional interactions.PloS one · 2012Article
- Protein-protein interaction reveals synergistic discrimination of cancer phenotype.Cancer informatics · 2010Article
- Synergy Disequilibrium Plots: graphical visualization of pairwise synergies and redundancies of SNPs with respect to a phenotype.Bioinformatics (Oxford, England) · 2009Article
- MIClique: An algorithm to identify differentially coexpressed disease gene subset from microarray data.Journal of biomedicine & biotechnology · 2009Article
- Identification of gene interactions associated with disease from gene expression data using synergy networks.BMC systems biology · 2008Article
- Computational analysis of the synergy among multiple interacting genes.Molecular systems biology · 2007Article
- An information-gain approach to detecting three-way epistatic interactions in genetic association studies.Journal of the American Medical Informatics Association : JAMIAArticle
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Authors and funding
2 authors.
Funding
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Abstract
Computational analysis of gene expression data from microarrays has been useful for medical diagnosis and prognosis. The ability to analyze such data at the level of biological modules, rather than individual genes, has been recognized as important for improving our understanding of disease-related pathways. It has proved difficult, however, to infer pathways from microarray data by deriving modules of multiple synergistically interrelated genes, rather than individual genes. Here we propose a systems-based approach called Entropy Minimization and Boolean Parsimony (EMBP) that identifies, directly from gene expression data, modules of genes that are jointly associated with disease. Furthermore, the technique provides insight into the underlying biomolecular logic by inferring a logic function connecting the joint expression levels in a gene module with the outcome of disease. Coupled with biological knowledge, this information can be useful for identifying disease-related pathways, suggesting potential therapeutic approaches for interfering with the functions of such pathways. We present an example providing such gene modules associated with prostate cancer from publicly available gene expression data, and we successfully validate the results on additional independently derived data. Our results indicate a link between prostate cancer and cellular damage from oxidative stress combined with inhibition of apoptotic mechanisms normally triggered by such damage.
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