ArticleGenetic epidemiology2011
Entropy-based information gain approaches to detect and to characterize gene-gene and gene-environment interactions/correlations of complex diseases.
Article in Genetic epidemiology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 2 of them syntheses that pooled it.
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Who cites it
37 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Transferring entropy to the realm of GxG interactions.Briefings in bioinformatics · 2018Pooled it
- Association between NQO1 C609T polymorphism and bladder cancer susceptibility: a systemic review and meta-analysis.Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine · 2013Pooled it
- XAI-Driven Intrusion Detection for Internet of Things Networks.Sensors (Basel, Switzerland) · 2026Article
- Speeding Up the Discovery of Optimal Feature Combinations for Omics Data Based on Pseudo-Kernel Function.Research square · 2026Article
- PrepCare: a two-stage framework for infectious disease prioritization and alerts for nursing preparedness.Frontiers in public health · 2026Article
- High-Dimensional Gene-Environment Interaction Analysis.Annual review of statistics and its application · 2025Article
- Identifying microbial drivers in biological phenotypes with a Bayesian network regression model.Ecology and evolution · 2024Article
- Revealing third-order interactions through the integration of machine learning and entropy methods in genomic studies.BioData mining · 2024Article
- Machine learning analyses reveal circadian clock features predictive of anxiety among UK biobank participants.Scientific reports · 2023Article
- Unified model-free interaction screening via CV-entropy filter.Computational statistics & data analysis · 2023Article
- Comparative analysis of tissue-specific genes in maize based on machine learning models: CNN performs technically best, LightGBM performs biologically soundest.Frontiers in genetics · 2023Article
- 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
- Genetic interactions effects for cancer disease identification using computational models: a review.Medical & biological engineering & computing · 2021Review
- 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
- A miRNA- and mRNA-seq-Based Feature Selection Approach for Kidney Cancer Biomakers.Cancer informatics · 2020Article
- ClusterMI: Detecting High-Order SNP Interactions Based on Clustering and Mutual Information.International journal of molecular sciences · 2018Article
- The paradox of intelligence: Heritability and malleability coexist in hidden gene-environment interplay.Psychological bulletin · 2018Review
- Bayesian reversible-jump for epistasis analysis in genomic studies.BMC genomics · 2016Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
For complex diseases, the relationship between genotypes, environment factors, and phenotype is usually complex and nonlinear. Our understanding of the genetic architecture of diseases has considerably increased over the last years. However, both conceptually and methodologically, detecting gene-gene and gene-environment interactions remains a challenge, despite the existence of a number of efficient methods. One method that offers great promises but has not yet been widely applied to genomic data is the entropy-based approach of information theory. In this article, we first develop entropy-based test statistics to identify two-way and higher order gene-gene and gene-environment interactions. We then apply these methods to a bladder cancer data set and thereby test their power and identify strengths and weaknesses. For two-way interactions, we propose an information gain (IG) approach based on mutual information. For three-ways and higher order interactions, an interaction IG approach is used. In both cases, we develop one-dimensional test statistics to analyze sparse data. Compared to the naive chi-square test, the test statistics we develop have similar or higher power and is robust. Applying it to the bladder cancer data set allowed to investigate the complex interactions between DNA repair gene single nucleotide polymorphisms, smoking status, and bladder cancer susceptibility. Although not yet widely applied, entropy-based approaches appear as a useful tool for detecting gene-gene and gene-environment interactions. The test statistics we develop add to a growing body methodologies that will gradually shed light on the complex architecture of common diseases.
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Registered trials
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