ReviewGenomics2012
Random forests for genomic data analysis.
Review in Genomics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 383 papers, 3 of them syntheses 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
383 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Radiomics-Based AI for the Diagnosis and Prognosis of Vessels Encapsulating Tumor Clusters in Hepatocellular Carcinoma: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Artificial intelligence for genomic science: a scoping review of concepts, architectures, applications, and open challenges.Frontiers in bioinformatics · 2026Pooled it
- Harnessing the power of artificial intelligence in predicting all-cause mortality in transcatheter aortic valve replacement: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2024Pooled it
- Elucidating the role of the gut microbiota in the physiological effects of dietary fiber.Microbiome · 2022Trial
- Integrative transcriptomic and single-cell analysis identifies anoikis-related molecular signatures in vascular calcification.Renal failure · 2026Article
- Construction of risk prediction models for psoriasis based on 45 dietary nutrients using machine learning and SHAP analysis.Asia Pacific journal of clinical nutrition · 2026Article
- Identification of ferroptosis hub gene cDKN1A relating to immune microenvironment in rheumatoid arthritis via bioinformatic analysis and experimental verification.Clinical rheumatology · 2026Article
- MRI-Based Radiomics Model for Predicting Eligibility for Conservative Management of Endometrial Cancer.Cancer medicine · 2026Article
- Key predictors of mortality in profound hyponatremia beyond the correction rate.Clinical kidney journal · 2026Article
- Restoring Metabolic-Inflammatory Homeostasis: Curcumin's Multi-Layered Defense Against Chondrocyte Dysfunction.Metabolites · 2026Article
- Review
- An interpretable machine learning framework for dog breed inference and ancestry decomposition.bioRxiv : the preprint server for biology · 2026Article
- Identification of potential biomarkers and therapeutic targets for liver cirrhosis based on Mendelian randomization and machine learning.Biochemistry and biophysics reports · 2026Article
- GWAS and genomic prediction of crown rust resistance in the southern US elite oat (Avena sativa L.) germplasm.The plant genome · 2026Article
- Honey bee nutritranscriptomics reveals key insights towards precision nutrition.Journal of advanced research · 2026Article
- Identification and Verification of SLC6A15 Involved in Keloid via Bioinformatics Analysis and Machine Learning.Biochemical genetics · 2026Article
- SRSF1 as a promising biomarker and key player in the pathogenesis of Alzheimer's disease.Molecular medicine reports · 2026Article
- Integrative analysis and experimental validation of dioxin-interacting genes reveal diagnostic and prognostic biomarkers in lung adenocarcinoma.Clinical and experimental medicine · 2026Article
- A quantitative proteomics dataset for assessment and prediction of low dose X-ray radiation exposure in mice.bioRxiv : the preprint server for biology · 2026Article
- A Machine Learning Study of Predictors of Homophobia Across the Globe.Archives of sexual behavior · 2026Article
323 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
2 authors.
Funding
Abstract
Random forests (RF) is a popular tree-based ensemble machine learning tool that is highly data adaptive, applies to "large p, small n" problems, and is able to account for correlation as well as interactions among features. This makes RF particularly appealing for high-dimensional genomic data analysis. In this article, we systematically review the applications and recent progresses of RF for genomic data, including prediction and classification, variable selection, pathway analysis, genetic association and epistasis detection, and unsupervised learning.
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.