ArticleBMC genetics2009
Neural networks for modeling gene-gene interactions in association studies.
Article in BMC genetics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Decoding the genomic symphony: unravelling brain disorders through data integration and machine learning.Molecular psychiatry · 2025Review
- Machine learning models to predict 6-month mortality risk in home-based hospice patients with advanced cancer.Asia-Pacific journal of oncology nursing · 2025Article
- Development of Nonlaboratory-Based Risk Prediction Models for Cardiovascular Diseases Using Conventional and Machine Learning Approaches.International journal of environmental research and public health · 2021Article
- Genome-wide variant-based study of genetic effects with the largest neuroanatomic coverage.BMC bioinformatics · 2021Article
- Mammalian SWI/SNF Complex Genomic Alterations and Immune Checkpoint Blockade in Solid Tumors.Cancer immunology research · 2020Article
- Robust genetic interaction analysis.Briefings in bioinformatics · 2019Review
- Aggregation of experts: an application in the field of "interactomics" (detection of interactions on the basis of genomic data).BMC bioinformatics · 2018Article
- Classifying Alzheimer's disease with brain imaging and genetic data using a neural network framework.Neurobiology of aging · 2018Article
- Tantalizing dilemma in risk prediction from disease scoring statistics.Briefings in functional genomics · 2018Review
- KNN-MDR: a learning approach for improving interactions mapping performances in genome wide association studies.BMC bioinformatics · 2017Article
- Ant colony optimisation of decision tree and contingency table models for the discovery of gene-gene interactions.IET systems biology · 2015Review
- Artificial neural network inference (ANNI): a study on gene-gene interaction for biomarkers in childhood sarcomas.PloS one · 2014Article
- A review for detecting gene-gene interactions using machine learning methods in genetic epidemiology.BioMed research international · 2013Article
- Challenges and opportunities in genome-wide environmental interaction (GWEI) studies.Human genetics · 2012Review
- Artificial neural networks modeling gene-environment interaction.BMC genetics · 2012Article
- Genomics in the post-GWAS era.Seminars in liver disease · 2011Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOur aim is to investigate the ability of neural networks to model different two-locus disease models. We conduct a simulation study to compare neural networks with two standard methods, namely logistic regression models and multifactor dimensionality reduction. One hundred data sets are generated for each of six two-locus disease models, which are considered in a low and in a high risk scenario. Two models represent independence, one is a multiplicative model, and three models are epistatic. For each data set, six neural networks (with up to five hidden neurons) and five logistic regression models (the null model, three main effect models, and the full model) with two different codings for the genotype information are fitted. Additionally, the multifactor dimensionality reduction approach is applied.
resultsThe results show that neural networks are more successful in modeling the structure of the underlying disease model than logistic regression models in most of the investigated situations. In our simulation study, neither logistic regression nor multifactor dimensionality reduction are able to correctly identify biological interaction.
conclusionsNeural networks are a promising tool to handle complex data situations. However, further research is necessary concerning the interpretation of their parameters.
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