ArticleIEEE Symposium on Computational Intelligence and Data Mining. IEEE Symposium on Computational Intelligence and Data Mining2007
Association Rule Discovery Has the Ability to Model Complex Genetic Effects.
Article in IEEE Symposium on Computational Intelligence and Data Mining. IEEE Symposium on Computational Intelligence and Data Mining, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Role of artificial intelligence in MS clinical practice.NeuroImage. Clinical · 2022Review
- Mining Complex Genetic Patterns Conferring Multiple Sclerosis Risk.International journal of environmental research and public health · 2021Article
- Biclustering of gene expression data by correlation-based scatter search.BioData mining · 2011Article
- 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
3 authors.
Funding
Abstract
Dramatic advances in genotyping technology have established a need for fast, flexible analysis methods for genetic association studies. Common complex diseases, such as Parkinson's disease or multiple sclerosis, are thought to involve an interplay of multiple genes working either independently or together to influence disease risk. Also, multiple underlying traits, each its own genetic basis may be defined together as a single disease. These effects - trait heterogeneity, locus heterogeneity, and gene-gene interactions (epistasis) - contribute to the complex architecture of common genetic diseases. Association Rule Discovery (ARD) searches for frequent itemsets to identify rule-based patterns in large scale data. In this study, we apply Apriori (an ARD algorithm) to simulated genetic data with varying degrees of complexity. Apriori using information difference to prior as a rule measure shows good power to detect functional effects in simulated cases of simple trait heterogeneity, trait heterogeneity and epistasis, and moderate power in cases of trait heterogeneity and locus heterogeneity. Also, we illustrate that bootstrapping the rule induction process does not considerably improve the power to detect these effects. These results show that ARD is a framework with sufficient flexibility to characterize complex genetic effects.
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Registered trials
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.