ArticleBriefings in bioinformatics2013
Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle?
Article in Briefings in bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 178 papers, 2 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
178 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View.Journal of medical Internet research · 2016Guideline
- The amniotic fluid cell-free transcriptome in spontaneous preterm labor.Scientific reports · 2021Trial
- 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
- The relative importance of neighborhood environment features in explaining preeclampsia risk using machine learning.Pregnancy (Hoboken, N.J.) · 2026Article
- Construction of an associative model for prolonged intensive care unit stay in sepsis patients combined with myocardial injury.Clinics (Sao Paulo, Brazil) · 2026Article
- Identification of IL1RN, MUC1, and SERPINA1 as key NET-related biomarkers in ulcerative colitis-associated intestinal fibrosis via bioinformatics and experimental validation.BMC gastroenterology · 2026Article
- Article
- Machine Learning Model to Predict Postmastectomy Breast Reconstruction Complications.JAMA network open · 2026Article
- Revisiting AI Interpretability in Precision Oncology: Why Predictive Accuracy Does Not Ensure Stable Feature Importance.Cancers · 2026Article
- Machine learning reveals an interleukin-33-associated immuno-metabolic axis defining recovery endotypes in severe combat trauma.Frontiers in immunology · 2026Article
- Primer on Modelling Approaches for Omics Data.Advances in experimental medicine and biology · 2026Article
- Combined machine learning identification and experimental validation of NDUFS8 and SUCLG1 as potential biomarkers for mechanical asphyxia.Frontiers in medicine · 2026Article
- Correlation between the reduction of IL-6 and TNF-α with muscle mass restoration in hyperthyroid patients post-radioactive iodine therapy.Frontiers in endocrinology · 2026Article
- Relationship of endothelial activation and stress index on concurrent acute kidney injury in patients with heart failure during intensive care unit: a competing risk model analysis.BMC cardiovascular disorders · 2025Observational
- Faecal metabolites as a readout of habitual diet capture dietary interactions with the gut microbiome.Nature communications · 2025Article
- Predicting sleep disturbance among patients with breast cancer in China through machine learning algorithms-a multi-site survey study.BMC psychiatry · 2025Article
- Fraction-based Linear Extrapolation (FLEX) Method for Predicting Human Pharmacokinetic Clearance: Advanced Allometric Scaling Method and Machine Learning Approach.Pharmaceutical research · 2025Article
- Prognostic Factors for Mortality Following Diaphragmatic Herniorrhaphy in Dogs and Cats: Multivariable Logistic Regression and Machine Learning Approaches.Veterinary sciences · 2025Article
- The association of albumin-corrected anion gap and acute kidney injury in heart failure patients: a competing risk model analysis.BMC cardiovascular disorders · 2025Article
118 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the Life Sciences 'omics' data is increasingly generated by different high-throughput technologies. Often only the integration of these data allows uncovering biological insights that can be experimentally validated or mechanistically modelled, i.e. sophisticated computational approaches are required to extract the complex non-linear trends present in omics data. Classification techniques allow training a model based on variables (e.g. SNPs in genetic association studies) to separate different classes (e.g. healthy subjects versus patients). Random Forest (RF) is a versatile classification algorithm suited for the analysis of these large data sets. In the Life Sciences, RF is popular because RF classification models have a high-prediction accuracy and provide information on importance of variables for classification. For omics data, variables or conditional relations between variables are typically important for a subset of samples of the same class. For example: within a class of cancer patients certain SNP combinations may be important for a subset of patients that have a specific subtype of cancer, but not important for a different subset of patients. These conditional relationships can in principle be uncovered from the data with RF as these are implicitly taken into account by the algorithm during the creation of the classification model. This review details some of the to the best of our knowledge rarely or never used RF properties that allow maximizing the biological insights that can be extracted from complex omics data sets using RF.
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.