ReviewFrontiers in genetics2019
Machine Learning SNP Based Prediction for Precision Medicine.
Review in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 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
90 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Pathway-Specific Polygenic Risk Scores Identify Obstructive Sleep Apnea-Related Pathways Differentially Moderating Genetic Susceptibility to Coronary Artery Disease.Circulation. Genomic and precision medicine · 2022Pooled it
- Machine learning for genetic prediction of psychiatric disorders: a systematic review.Molecular psychiatry · 2021Pooled it
- Pooled it
- Exploring the use of machine and deep learning in genome-wide association studies: a comprehensive review.BioData mining · 2026Review
- Data Representation Bias and Conditional Distribution Shift Drive Predictive Performance Disparities in Multi-Population Machine Learning.bioRxiv : the preprint server for biology · 2026Article
- CLCNet: a contrastive learning and chromosome-aware network for genomic prediction in plants.Briefings in bioinformatics · 2026Article
- The role of artificial intelligence in early detection and risk prediction of ischemic heart disease.Annals of medicine and surgery (2012) · 2026Review
- Evaluating genetic-based disease prediction approaches through simulation.Human genetics · 2026Article
- Integrating genetic, epigenetic, and clinical signatures via machine learning for robust prediction of leflunomide response in rheumatoid arthritis: a multi-center validation study.Frontiers in immunology · 2026Article
- A machine learning-derived genomic dataset from bacteria frequently reported as probiotics.Frontiers in bioinformatics · 2026Article
- Using machine learning to predict and analyze complex trait diseases: Lessons from a simple abstract model.PloS one · 2026Article
- Article
- Exploring Random Forest in Genetic Risk Score Construction.Genetic epidemiology · 2025Article
- Core Promoters of Pig SOD2 Gene and Its Expression Regulation by DNA Methylation.Veterinary sciences · 2025Article
- Advanced machine learning techniques for coronary artery disease risk prediction in early middle-aged adults.BMC medical genomics · 2025Article
- Precision in prediction: tailoring machine learning models for breast cancer missense variants pathogenicity prediction.Briefings in bioinformatics · 2025Article
- BRCAGenie: A machine learning-driven 43-gene polygenic risk score model for precision prediction of breast cancer survival.Journal of translational medicine · 2025Article
- Ge-SAND: an explainable deep learning-driven framework for disease risk prediction by uncovering complex genetic interactions in parallel.BMC genomics · 2025Article
- A systematic in-silico functional and structural analysis reveals deleterious missense nsSNPs in the humanMolecular biology research communications · 2025Article
- AI, agentic models and lab automation for scientific discovery - the beginning of scAInce.Frontiers in artificial intelligence · 2025Review
30 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the past decade, precision genomics based medicine has emerged to provide tailored and effective healthcare for patients depending upon their genetic features. Genome Wide Association Studies have also identified population based risk genetic variants for common and complex diseases. In order to meet the full promise of precision medicine, research is attempting to leverage our increasing genomic understanding and further develop personalized medical healthcare through ever more accurate disease risk prediction models. Polygenic risk scoring and machine learning are two primary approaches for disease risk prediction. Despite recent improvements, the results of polygenic risk scoring remain limited due to the approaches that are currently used. By contrast, machine learning algorithms have increased predictive abilities for complex disease risk. This increase in predictive abilities results from the ability of machine learning algorithms to handle multi-dimensional data. Here, we provide an overview of polygenic risk scoring and machine learning in complex disease risk prediction. We highlight recent machine learning application developments and describe how machine learning approaches can lead to improved complex disease prediction, which will help to incorporate genetic features into future personalized healthcare. Finally, we discuss how the future application of machine learning prediction models might help manage complex disease by providing tissue-specific targets for customized, preventive interventions.
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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.