ArticleBMC systems biology2008
Identification of gene interactions associated with disease from gene expression data using synergy networks.
Article in BMC systems biology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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30 citing papers in PubMed, 71 citations in OpenAlex.
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- Uncovering Effective Explanations for Interactive Genomic Data Analysis.Patterns (New York, N.Y.) · 2020Article
- High dimensional model representation of log-likelihood ratio: binary classification with expression data.BMC bioinformatics · 2020Article
- Research on Multi-Time-Delay Gene Regulation Network Based on Fuzzy Label Propagation.Journal of healthcare engineering · 2020Article
- A data-driven interactome of synergistic genes improves network-based cancer outcome prediction.PLoS computational biology · 2019Article
- Feature selection with interactions in logistic regression models using multivariate synergies for a GWAS application.BMC genomics · 2018Article
- A fast approach to detect gene-gene synergy.Scientific reports · 2017Article
- xSyn: A Software Tool for Identifying Sophisticated 3-Way Interactions From Cancer Expression Data.Cancer informatics · 2017Article
- Discovering Pair-wise Synergies in Microarray Data.Scientific reports · 2016Article
- Pathway mapping and development of disease-specific biomarkers: protein-based network biomarkers.Journal of cellular and molecular medicine · 2015Review
- Learning dysregulated pathways in cancers from differential variability analysis.Cancer informatics · 2014Review
- Pathway analyses and understanding disease associations.Current genetic medicine reports · 2013Article
- Feature ranking based on synergy networks to identify prognostic markers in DPT-1.EURASIP journal on bioinformatics & systems biology · 2013Article
- Inference of gene regulatory networks from genetic perturbations with linear regression model.PloS one · 2013Article
- Harnessing the complexity of gene expression data from cancer: from single gene to structural pathway methods.Biology direct · 2012Review
- Network biology methods integrating biological data for translational science.Briefings in bioinformatics · 2012Review
- Gene interaction enrichment and network analysis to identify dysregulated pathways and their interactions in complex diseases.BMC systems biology · 2012Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAnalysis of microarray data has been used for the inference of gene-gene interactions. If, however, the aim is the discovery of disease-related biological mechanisms, then the criterion for defining such interactions must be specifically linked to disease.
resultsHere we present a computational methodology that jointly analyzes two sets of microarray data, one in the presence and one in the absence of a disease, identifying gene pairs whose correlation with disease is due to cooperative, rather than independent, contributions of genes, using the recently developed information theoretic measure of synergy. High levels of synergy in gene pairs indicates possible membership of the two genes in a shared pathway and leads to a graphical representation of inferred gene-gene interactions associated with disease, in the form of a "synergy network." We apply this technique on a set of publicly available prostate cancer expression data and successfully validate our results, confirming that they cannot be due to pure chance and providing a biological explanation for gene pairs with exceptionally high synergy.
conclusionThus, synergy networks provide a computational methodology helpful for deriving "disease interactomes" from biological data. When coupled with additional biological knowledge, they can also be helpful for deciphering biological mechanisms responsible for disease.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.