ReviewHuman genetics2012
Risk estimation and risk prediction using machine-learning methods.
Review in Human genetics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 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
59 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The usefulness of artificial intelligence in breast reconstruction: a systematic review.Breast cancer (Tokyo, Japan) · 2024Pooled it
- Swept source optical coherence tomography to early detect multiple sclerosis disease. The use of machine learning techniques.PloS one · 2019Trial
- Prediction of Lithium Therapeutic Target Attainment in Adolescents Using an XGBoost Model: A Single-Center Retrospective Study.Drug design, development and therapy · 2026Article
- Machine-learning prediction of pre-dose pharmacokinetics optimizes initial vancomycin dosing in critically ill children.Frontiers in pharmacology · 2026Article
- Artificial Intelligence in Breast Reconstruction: A Narrative Review.Medicina (Kaunas, Lithuania) · 2025Review
- Review
- Machine learning-based models for predicting mortality and acute kidney injury in critical pulmonary embolism.BMC cardiovascular disorders · 2023Article
- Comparison and development of machine learning for thalidomide-induced peripheral neuropathy prediction of refractory Crohn's disease in Chinese population.World journal of gastroenterology · 2023Article
- Classification of imbalanced data using machine learning algorithms to predict the risk of renal graft failures in Ethiopia.BMC medical informatics and decision making · 2023Article
- Functional variants identify sex-specific genes and pathways in Alzheimer's Disease.Nature communications · 2023Article
- Deep Learning Framework for Complex Disease Risk Prediction Using Genomic Variations.Sensors (Basel, Switzerland) · 2023Article
- Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review.Archives of computational methods in engineering : state of the art reviews · 2023Review
- The effect of Shengmai injection in patients with coronary heart disease in real world and its personalized medicine research using machine learning techniques.Frontiers in pharmacology · 2023Article
- Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review.Rheumatology and therapy · 2022Review
- Evaluation of tree-based statistical learning methods for constructing genetic risk scores.BMC bioinformatics · 2022Article
- A Genome-Wide Association Study and Machine-Learning Algorithm Analysis on the Prediction of Facial Phenotypes by Genotypes in Korean Women.Clinical, cosmetic and investigational dermatology · 2022Article
- An individualized medication model of sodium valproate for patients with bipolar disorder based on machine learning and deep learning techniques.Frontiers in pharmacology · 2022Article
- A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction.Frontiers in bioinformatics · 2022Review
- Predicting Lapatinib Dose Regimen Using Machine Learning and Deep Learning Techniques Based on a Real-World Study.Frontiers in oncology · 2022Article
- A Prediction Model for Tacrolimus Daily Dose in Kidney Transplant Recipients With Machine Learning and Deep Learning Techniques.Frontiers in medicine · 2022Article
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
After an association between genetic variants and a phenotype has been established, further study goals comprise the classification of patients according to disease risk or the estimation of disease probability. To accomplish this, different statistical methods are required, and specifically machine-learning approaches may offer advantages over classical techniques. In this paper, we describe methods for the construction and evaluation of classification and probability estimation rules. We review the use of machine-learning approaches in this context and explain some of the machine-learning algorithms in detail. Finally, we illustrate the methodology through application to a genome-wide association analysis on rheumatoid arthritis.
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.