ArticleBioData mining2016
r2VIM: A new variable selection method for random forests in genome-wide association studies.
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 28 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed.
- Enhancement of Inpatient Mortality Prognostication With Machine Learning in a Prospective Global Cohort of Patients With Cirrhosis With External Validation.Gastroenterology · 2026Article
- Disentangling soybean GxE effects in an integrated genomic prediction and machine learning-GWAS workflow.Plant methods · 2025Article
- Combating Root-Knot Nematodes (Plants (Basel, Switzerland) · 2025Review
- Modeling Chickpea Productivity with Artificial Image Objects and Convolutional Neural Network.Plants (Basel, Switzerland) · 2024Article
- Out of (the) bag-encoding categorical predictors impacts out-of-bag samples.PeerJ. Computer science · 2024Article
- Exploitation of surrogate variables in random forests for unbiased analysis of mutual impact and importance of features.Bioinformatics (Oxford, England) · 2023Article
- Establishment and Validation of Fourier Transform Infrared Spectroscopy (FT-MIR) Methodology for the Detection of Linoleic Acid in Buffalo Milk.Foods (Basel, Switzerland) · 2023Article
- Modeling of Flowering Time inPlants (Basel, Switzerland) · 2022Article
- Epi-MEIF: detecting higher order epistatic interactions for complex traits using mixed effect conditional inference forests.Nucleic acids research · 2022Article
- What makes a good prediction? Feature importance and beginning to open the black box of machine learning in genetics.Human genetics · 2022Review
- A Random Forest-Based Genome-Wide Scan Reveals Fertility-Related Candidate Genes and Potential Inter-Chromosomal Epistatic Regions Associated With Age at First Calving in Nellore Cattle.Frontiers in genetics · 2022Article
- Exploring Machine Learning Algorithms to Unveil Genomic Regions Associated With Resistance to Southern Root-Knot Nematode in Soybeans.Frontiers in plant science · 2022Article
- Article
- 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
- Application of random forest based approaches to surface-enhanced Raman scattering data.Scientific reports · 2020Article
- Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci.Frontiers in genetics · 2020Review
- TSLRF: Two-Stage Algorithm Based on Least Angle Regression and Random Forest in genome-wide association studies.Scientific reports · 2019Article
- Evaluation of variable selection methods for random forests and omics data sets.Briefings in bioinformatics · 2019Article
- Genome-wide association study of myocardial infarction, atrial fibrillation, acute stroke, acute kidney injury and delirium after cardiac surgery - a sub-analysis of the RIPHeart-Study.BMC cardiovascular disorders · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
backgroundMachine learning methods and in particular random forests (RFs) are a promising alternative to standard single SNP analyses in genome-wide association studies (GWAS). RFs provide variable importance measures (VIMs) to rank SNPs according to their predictive power. However, in contrast to the established genome-wide significance threshold, no clear criteria exist to determine how many SNPs should be selected for downstream analyses.
resultsWe propose a new variable selection approach, recurrent relative variable importance measure (r2VIM). Importance values are calculated relative to an observed minimal importance score for several runs of RF and only SNPs with large relative VIMs in all of the runs are selected as important. Evaluations on simulated GWAS data show that the new method controls the number of false-positives under the null hypothesis. Under a simple alternative hypothesis with several independent main effects it is only slightly less powerful than logistic regression. In an experimental GWAS data set, the same strong signal is identified while the approach selects none of the SNPs in an underpowered GWAS.
conclusionsThe novel variable selection method r2VIM is a promising extension to standard RF for objectively selecting relevant SNPs in GWAS while controlling the number of false-positive results.
Indexed as
Identifiers
What OpenQuestion holds
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.