SynthesisBriefings in bioinformatics2018
Transferring entropy to the realm of GxG interactions.
Synthesis in Briefings in bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Research on multi-trait genome association study method based on Shannon information entropy.BMC bioinformatics · 2026Article
- Turning heterogeneity of statistical epistasis networks to an advantage.Briefings in bioinformatics · 2026Article
- Review on GPU accelerated methods for genome-wide SNP-SNP interactions.Molecular genetics and genomics : MGG · 2024Review
- Revealing third-order interactions through the integration of machine learning and entropy methods in genomic studies.BioData mining · 2024Article
- Detecting gene-gene interactions from GWAS using diffusion kernel principal components.BMC bioinformatics · 2022Article
- Optimized permutation testing for information theoretic measures of multi-gene interactions.BMC bioinformatics · 2021Article
- Information Theory in Computational Biology: Where We Stand Today.Entropy (Basel, Switzerland) · 2020Article
- Modified entropy-based procedure detects gene-gene-interactions in unconventional genetic models.BMC medical genomics · 2020Article
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2 authors.
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Abstract
Genome-wide association studies are moving to genome-wide interaction studies, as the genetic background of many diseases appears to be more complex than previously supposed. Thus, many statistical approaches have been proposed to detect gene-gene (GxG) interactions, among them numerous information theory-based methods, inspired by the concept of entropy. These are suggested as particularly powerful and, because of their nonlinearity, as better able to capture nonlinear relationships between genetic variants and/or variables. However, the introduced entropy-based estimators differ to a surprising extent in their construction and even with respect to the basic definition of interactions. Also, not every entropy-based measure for interaction is accompanied by a proper statistical test. To shed light on this, a systematic review of the literature is presented answering the following questions: (1) How are GxG interactions defined within the framework of information theory? (2) Which entropy-based test statistics are available? (3) Which underlying distribution do the test statistics follow? (4) What are the given strengths and limitations of these test statistics?
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