ArticleBMC bioinformatics2016
Do little interactions get lost in dark random forests?
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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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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Who cites it
44 citing papers in PubMed.
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- The use of centralized waiting lists for attachment to family physicians in Quebec, Canada: predictive factors and association with non-urgent emergency department use.BMC health services research · 2026Article
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- Downscaling local distribution of cattle over Guadeloupe archipelago: An adapted method for disaggregating census data.PloS one · 2026Article
- Faecal metabolites as a readout of habitual diet capture dietary interactions with the gut microbiome.Nature communications · 2025Article
- Decoding the genomic symphony: unravelling brain disorders through data integration and machine learning.Molecular psychiatry · 2025Review
- Predictive models of the genetic bases underlying budding yeast fitness in multiple environments.bioRxiv : the preprint server for biology · 2025Article
- Chemical, Morphological, and Phenological Traits of Blueberry Cultivars Predict Susceptibility to A Pollinator-Vectored Fungal Pathogen.Journal of chemical ecology · 2025Article
- Asymmetric Inter-Hemisphere Communication Contributes to Speech Acquisition of Toddlers with Cochlear Implants.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Exploring genomic feature selection: A comparative analysis of GWAS and machine learning algorithms in a large-scale soybean dataset.The plant genome · 2025Article
- Prediction of individual treatment allocation between electroconvulsive therapy or ketamine using the Personalized Advantage Index.NPJ digital medicine · 2025Article
- Insights into heart failure metabolite markers through explainable machine learning.Computational and structural biotechnology journal · 2025Article
- Bipolar disorder: Construction and analysis of a joint diagnostic model using random forest and feedforward neural networks.IBRO neuroscience reports · 2024Article
- The association between urban land use and depressive symptoms in young adulthood: a FinnTwin12 cohort study.Journal of exposure science & environmental epidemiology · 2024Article
- Statistical and Machine Learning Analysis in Brain-Imaging Genetics: A Review of Methods.Behavior genetics · 2024Review
- PMGeoHealth · 2024Article
- Individual Prediction of Optimal Treatment Allocation Between Electroconvulsive Therapy or Ketamine using the Personalized Advantage Index.Research square · 2023Article
- Exploring the potential of incremental feature selection to improve genomic prediction accuracy.Genetics, selection, evolution : GSE · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRandom forests have often been claimed to uncover interaction effects. However, if and how interaction effects can be differentiated from marginal effects remains unclear. In extensive simulation studies, we investigate whether random forest variable importance measures capture or detect gene-gene interactions. With capturing interactions, we define the ability to identify a variable that acts through an interaction with another one, while detection is the ability to identify an interaction effect as such.
resultsOf the single importance measures, the Gini importance captured interaction effects in most of the simulated scenarios, however, they were masked by marginal effects in other variables. With the permutation importance, the proportion of captured interactions was lower in all cases. Pairwise importance measures performed about equal, with a slight advantage for the joint variable importance method. However, the overall fraction of detected interactions was low. In almost all scenarios the detection fraction in a model with only marginal effects was larger than in a model with an interaction effect only.
conclusionsRandom forests are generally capable of capturing gene-gene interactions, but current variable importance measures are unable to detect them as interactions. In most of the cases, interactions are masked by marginal effects and interactions cannot be differentiated from marginal effects. Consequently, caution is warranted when claiming that random forests uncover interactions.
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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.