ArticlePloS one2009
Clique-finding for heterogeneity and multidimensionality in biomarker epidemiology research: the CHAMBER algorithm.
Article in PloS one, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 6 citations in OpenAlex.
- Germline genetic variation and treatment response on CCG-1891.Pediatric blood & cancer · 2012Article
- Transactional database transformation and its application in prioritizing human disease genes.IEEE/ACM transactions on computational biology and bioinformaticsArticle
- Role of genetic heterogeneity and epistasis in bladder cancer susceptibility and outcome: a learning classifier system approach.Journal of the American Medical Informatics Association : JAMIAArticle
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundCommonly-occurring disease etiology may involve complex combinations of genes and exposures resulting in etiologic heterogeneity. We present a computational algorithm that employs clique-finding for heterogeneity and multidimensionality in biomedical and epidemiological research (the "CHAMBER" algorithm). METHODOLOGY/PRINCIPAL
findingsThis algorithm uses graph-building to (1) identify genetic variants that influence disease risk and (2) predict individuals at risk for disease based on inherited genotype. We use a set-covering algorithm to identify optimal cliques and a Boolean function that identifies etiologically heterogeneous groups of individuals. We evaluated this approach using simulated case-control genotype-disease associations involving two- and four-gene patterns. The CHAMBER algorithm correctly identified these simulated etiologies. We also used two population-based case-control studies of breast and endometrial cancer in African American and Caucasian women considering data on genotypes involved in steroid hormone metabolism. We identified novel patterns in both cancer sites that involved genes that sulfate or glucuronidate estrogens or catecholestrogens. These associations were consistent with the hypothesized biological functions of these genes. We also identified cliques representing the joint effect of multiple candidate genes in all groups, suggesting the existence of biologically plausible combinations of hormone metabolism genes in both breast and endometrial cancer in both races.
conclusionsThe CHAMBER algorithm may have utility in exploring the multifactorial etiology and etiologic heterogeneity in complex disease.
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Registered trials
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