ArticleBioData mining2016
Detecting gene-gene interactions using a permutation-based random forest method.
Article in BioData mining, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Genetic analysis of neurodegenerative diseases.The Journal of clinical investigation · 2026Review
- Decoding the genomic symphony: unravelling brain disorders through data integration and machine learning.Molecular psychiatry · 2025Review
- Protein interactions, network pharmacology, and machine learning work together to predict genes linked to mitochondrial dysfunction in hypertrophic cardiomyopathy.Scientific reports · 2025Article
- Machine learning-driven identification of critical gene programs and key transcription factors in migraine.The journal of headache and pain · 2025Article
- Inferring gene regulatory networks with graph convolutional network based on causal feature reconstruction.Scientific reports · 2024Article
- Article
- A simulative deep learning model of SNP interactions on chromosome 19 for predicting Alzheimer's disease risk and rates of disease progression.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Article
- Epistasis regulates genetic control of cardiac hypertrophy.Research square · 2023Article
- BRASS: Permutation methods for binary traits in genetic association studies with structured samples.PLoS genetics · 2023Article
- Article
- What makes a good prediction? Feature importance and beginning to open the black box of machine learning in genetics.Human genetics · 2022Review
- RIL-StEp: epistasis analysis of rice recombinant inbred lines reveals candidate interacting genes that control seed hull color and leaf chlorophyll content.G3 (Bethesda, Md.) · 2021Article
- Genetic interactions effects for cancer disease identification using computational models: a review.Medical & biological engineering & computing · 2021Review
- A zero altered Poisson random forest model for genomic-enabled prediction.G3 (Bethesda, Md.) · 2021Article
- A comparison of methods for interpreting random forest models of genetic association in the presence of non-additive interactions.BioData mining · 2021Article
- Article
- Brief Survey on Machine Learning in Epistasis.Methods in molecular biology (Clifton, N.J.) · 2021Review
- Gene Mutation Classification through Text Evidence Facilitating Cancer Tumour Detection.Journal of healthcare engineering · 2021Article
- Genome Wide Epistasis Study of On-Statin Cardiovascular Events with Iterative Feature Reduction and Selection.Journal of personalized medicine · 2020Article
- binomialRF: interpretable combinatoric efficiency of random forests to identify biomarker interactions.BMC bioinformatics · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundIdentifying gene-gene interactions is essential to understand disease susceptibility and to detect genetic architectures underlying complex diseases. Here, we aimed at developing a permutation-based methodology relying on a machine learning method, random forest (RF), to detect gene-gene interactions. Our approach called permuted random forest (pRF) which identified the top interacting single nucleotide polymorphism (SNP) pairs by estimating how much the power of a random forest classification model is influenced by removing pairwise interactions.
resultsWe systematically tested our approach on a simulation study with datasets possessing various genetic constraints including heritability, number of SNPs, sample size, etc. Our methodology showed high success rates for detecting the interaction SNP pair. We also applied our approach to two bladder cancer datasets, which showed consistent results with well-studied methodologies, such as multifactor dimensionality reduction (MDR) and statistical epistasis network (SEN). Furthermore, we built permuted random forest networks (PRFN), in which we used nodes to represent SNPs and edges to indicate interactions.
conclusionsWe successfully developed a scale-invariant methodology to detect pure gene-gene interactions based on permutation strategies and the machine learning method random forest. This methodology showed great potential to be used for detecting gene-gene interactions to study underlying genetic architectures in a scale-free way, which could be benefit to uncover the complex disease mechanisms.
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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.