ArticleBioMed research international2013
A review for detecting gene-gene interactions using machine learning methods in genetic epidemiology.
Article in BioMed research international, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 71 citations in OpenAlex.
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- Predicting the hub interactome of COVID-19 and oral squamous cell carcinoma: uncovering ALDH-mediated Wnt/β-catenin pathway activation via salivary inflammatory proteins.Scientific reports · 2025Article
- Biomedical literature mining: graph kernel-based learning for gene-gene interaction extraction.European journal of medical research · 2024Article
- Causative Classification of Ischemic Stroke by the Machine Learning Algorithm Random Forests.Frontiers in aging neuroscience · 2022Article
- Review
- Genome-wide variant-based study of genetic effects with the largest neuroanatomic coverage.BMC bioinformatics · 2021Article
- A comparison of methods for interpreting random forest models of genetic association in the presence of non-additive interactions.BioData mining · 2021Article
- A Genetic Variation in the Y Chromosome Among Modern Japanese Males Related to Several Physiological and Psychological Characteristics.Frontiers in behavioral neuroscience · 2021Article
- Gene-Environment Interaction: A Variable Selection Perspective.Methods in molecular biology (Clifton, N.J.) · 2021Review
- Estimating the heritability of psychological measures in the Human Connectome Project dataset.PloS one · 2020Article
- Predictive modeling of schizophrenia from genomic data: Comparison of polygenic risk score with kernel support vector machines approach.American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics · 2019Article
- Improving pharmacogenetic prediction of extrapyramidal symptoms induced by antipsychotics.Translational psychiatry · 2018Article
- A Machine Learning Approach to Identify NIH-Funded Applied Prevention Research.American journal of preventive medicine · 2018Article
- Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data.Briefings in bioinformatics · 2018Review
- Aggregation of experts: an application in the field of "interactomics" (detection of interactions on the basis of genomic data).BMC bioinformatics · 2018Article
- Machine learning-based identification of genetic interactions from heterogeneous gene expression profiles.PloS one · 2018Article
- Real-time Protection of Genomic Data Sharing in Beacon Services.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2018Article
- Blood Gene Expression Profile Predicts Response to Antipsychotics.Frontiers in molecular neuroscience · 2018Article
- KNN-MDR: a learning approach for improving interactions mapping performances in genome wide association studies.BMC bioinformatics · 2017Article
- Integrating Genetic, Neuropsychological and Neuroimaging Data to Model Early-Onset Obsessive Compulsive Disorder Severity.PloS one · 2016Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs), support vector machine (SVM), and random forests (RFs) in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease.
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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.