ReviewStatistical applications in genetics and molecular biology2011
Random forests for genetic association studies.
Review in Statistical applications in genetics and molecular biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 papers, 1 of them a synthesis that pooled it.
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
98 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for genomic science: a scoping review of concepts, architectures, applications, and open challenges.Frontiers in bioinformatics · 2026Pooled it
- Using the random forest method to detect a response shift in the quality of life of multiple sclerosis patients: a cohort study.BMC medical research methodology · 2013Trial
- Application Progress of Machine Learning in Prognostic Prediction of Percutaneous Coronary Intervention: A Systematic Review.Journal of clinical medicine research · 2026Review
- Uncontrolled Hypertension Prevalence, Characteristic Profiles, and Risk of Cardiorenal and Metabolic Outcomes: Insights from the EnligHTN Study.Advances in therapy · 2026Article
- 2NPLGBM: a genomic model that merges the strengths of classical and machine learning methods in genomic prediction.Plant methods · 2026Article
- DNA sequence contamination analyzer (DNASCAN): a supervised analysis toolkit for detecting and removing DNA contaminants.BMC bioinformatics · 2026Article
- Development and comparison of machine learning models for predicting postoperative ileus after posterior thoracolumbar fracture surgery.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Development and Validation of a Machine Learning-Based Prediction Model for Cardiovascular Disease in Patients with Metabolic Dysfunction-Associated Fatty Liver Disease.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2026Article
- Article
- Polygenic risk scores: Navigating the future of precision medicine through economic, ethical, and scientific advancements.iScience · 2026Review
- Enhancing genomic prediction of wheat resistance to fusarium head blight through integration of passive resistance traits.Frontiers in plant science · 2026Article
- Article
- Using Machine Learning to Analyze Molecular Dynamics Simulations of Biomolecules.The journal of physical chemistry. B · 2025Article
- Comparison of Random Forest and Stepwise Regression for Variable Selection Using Low Prevalence Predictors: A case Study in Paediatric Sepsis.Maternal and child health journal · 2025Article
- optRF: Optimising random forest stability by determining the optimal number of trees.BMC bioinformatics · 2025Article
- EnSCAN: ENsemble Scoring for prioritizing CAusative variaNts across multiplatform GWASs for late-onset alzheimer's disease.BioData mining · 2025Article
- Article
- Estimating forest aboveground carbon sink based on landsat time series and its response to climate change.Scientific reports · 2025Article
- Beyond sequence: A physics-informed machine learning framework for predicting DNA mutations.Computational and structural biotechnology journal · 2025Article
- Prognostic factors in patients with gastrointestinal perforation under the acute care surgery model : a retrospective cohort study.BMC surgery · 2024Article
38 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The Random Forests (RF) algorithm has become a commonly used machine learning algorithm for genetic association studies. It is well suited for genetic applications since it is both computationally efficient and models genetic causal mechanisms well. With its growing ubiquity, there has been inconsistent and less than optimal use of RF in the literature. The purpose of this review is to breakdown the theoretical and statistical basis of RF so that practitioners are able to apply it in their work. An emphasis is placed on showing how the various components contribute to bias and variance, as well as discussing variable importance measures. Applications specific to genetic studies are highlighted. To provide context, RF is compared to other commonly used machine learning algorithms.
Indexed as
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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.