ReviewFrontiers in bioinformatics2022
A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction.
Review in Frontiers in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 272 papers, 8 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
272 citing papers in PubMed, 8 syntheses or guidelines pooled it.
- Predicting disease outcomes from remote monitoring using machine learning: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Machine learning in the diagnosis and prognosis of transient ischaemic attack: a systematic review.BMC neurology · 2026Pooled it
- Risk prediction models for oral frailty in older adults: a scoping review.Frontiers in medicine · 2026Pooled it
- Revolutionizing sepsis diagnosis using machine learning and deep learning models: a systematic literature review.BMC infectious diseases · 2025Pooled it
- Clinical prediction models for postoperative blood transfusion after total knee arthroplasty: a systematic review and meta-analysis.BMC musculoskeletal disorders · 2025Pooled it
- Identification of key proteins and pathways in myocardial infarction using machine learning approaches.Scientific reports · 2025Pooled it
- The Impact and Role of Artificial Intelligence (AI) in Healthcare: Systematic Review.Current topics in medicinal chemistry · 2025Pooled it
- Artificial intelligence for surgical outcome prediction in glaucoma: a systematic review.Frontiers in big data · 2025Pooled it
- Proteomic and machine learning analysis predicts treatment response signatures in Myasthenia Gravis.Journal of translational medicine · 2026Trial
- Development and validation of a machine learning-driven framework for differentiating pediatric bronchopneumonia from lobar pneumonia: a multicenter investigation.European journal of medical research · 2025Trial
- The Utility of Machine Learning-Enhanced Developmental Cascade Models in Prevention Science.Prevention science : the official journal of the Society for Prevention Research · 2026Article
- Ultrasound Radiomics for Hashimoto's Thyroiditis: State of the Art and Future Perspectives.Journal of clinical ultrasound : JCU · 2026Review
- A simulation-based comparison of Boruta, LASSO, and Elastic Net for variable selection in logistic regression, with an ovarian cancer miRNA application.BMC medical research methodology · 2026Article
- Knowledge-guided learning with curated prior genetic biomarkers for robust model interpretation.Bioinformatics (Oxford, England) · 2026Article
- A machine learning approach to conglomerate multi-domain features of cardiac aging.European heart journal. Digital health · 2026Article
- Exploring the use of machine and deep learning in genome-wide association studies: a comprehensive review.BioData mining · 2026Review
- The molecular ICU: a primer on omics, informatics and the future of precision critical care.Critical care (London, England) · 2026Review
- AHGA-SA: A Novel Adaptive Hybrid Framework for Feature Selection in IoT-Oriented Intrusion Detection with Explainable AI.Sensors (Basel, Switzerland) · 2026Article
- OpenSim-Umberger-Based Metabolic Power Stratification During the Sit-to-Walk Transition Using Interpretable Ensemble Learning.Bioengineering (Basel, Switzerland) · 2026Article
- Integrating artificial intelligence and multi-omics data for precision oncology in endometrial cancer: a narrative review.Functional & integrative genomics · 2026Review
212 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning has shown utility in detecting patterns within large, unstructured, and complex datasets. One of the promising applications of machine learning is in precision medicine, where disease risk is predicted using patient genetic data. However, creating an accurate prediction model based on genotype data remains challenging due to the so-called "curse of dimensionality" (i.e., extensively larger number of features compared to the number of samples). Therefore, the generalizability of machine learning models benefits from feature selection, which aims to extract only the most "informative" features and remove noisy "non-informative," irrelevant and redundant features. In this article, we provide a general overview of the different feature selection methods, their advantages, disadvantages, and use cases, focusing on the detection of relevant features (i.e., SNPs) for disease risk prediction.
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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.