ArticleBMC research notes2008
Power of grammatical evolution neural networks to detect gene-gene interactions in the presence of error.
Article in BMC research notes, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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.
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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
15 citing papers in PubMed.
- Considerations in the search for epistasis.Genome biology · 2024Review
- Searching for gene-gene interactions through variance quantitative trait loci of 29 continuous Taiwan Biobank phenotypes.Frontiers in genetics · 2024Article
- Genetic heterogeneity: Challenges, impacts, and methods through an associative lens.Genetic epidemiology · 2022Review
- Gene-gene interaction: the curse of dimensionality.Annals of translational medicine · 2019Review
- An empirical fuzzy multifactor dimensionality reduction method for detecting gene-gene interactions.BMC genomics · 2017Article
- ATHENA: a tool for meta-dimensional analysis applied to genotypes and gene expression data to predict HDL cholesterol levels.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2013Article
- A review for detecting gene-gene interactions using machine learning methods in genetic epidemiology.BioMed research international · 2013Article
- A comparison of internal model validation methods for multifactor dimensionality reduction in the case of genetic heterogeneity.BMC research notes · 2012Article
- The genetics of alcohol dependence: advancing towards systems-based approaches.Drug and alcohol dependence · 2012Review
- Integrating heterogeneous high-throughput data for meta-dimensional pharmacogenomics and disease-related studies.Pharmacogenomics · 2012Article
- Performance analysis of novel methods for detecting epistasis.BMC bioinformatics · 2011Article
- Using biological knowledge to uncover the mystery in the search for epistasis in genome-wide association studies.Annals of human genetics · 2011Review
- Article
- Neural networks for modeling gene-gene interactions in association studies.BMC genetics · 2009Article
- Methods for optimizing statistical analyses in pharmacogenomics research.Expert review of clinical pharmacology · 2009Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundWith the advent of increasingly efficient means to obtain genetic information, a great insurgence of data has resulted, leading to the need for methods for analyzing this data beyond that of traditional parametric statistical approaches. Recently we introduced Grammatical Evolution Neural Network (GENN), a machine-learning approach to detect gene-gene or gene-environment interactions, also known as epistasis, in high dimensional genetic epidemiological data. GENN has been shown to be highly successful in a range of simulated data, but the impact of error common to real data is unknown. In the current study, we examine the power of GENN to detect interesting interactions in the presence of noise due to genotyping error, missing data, phenocopy, and genetic heterogeneity. Additionally, we compare the performance of GENN to that of another computational method - Multifactor Dimensionality Reduction (MDR).
findingsGENN is extremely robust to missing data and genotyping error. Phenocopy in a dataset reduces the power of both GENN and MDR. GENN is reasonably robust to genetic heterogeneity and find that in some cases GENN has substantially higher power than MDR to detect functional loci in the presence of genetic heterogeneity.
conclusionGENN is a promising method to detect gene-gene interaction, even in the presence of common types of error found in real data.
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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.