ReviewHuman genetics2020
Statistical learning approaches in the genetic epidemiology of complex diseases.
Review in Human genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning for genetic prediction of psychiatric disorders: a systematic review.Molecular psychiatry · 2021Pooled it
- Current methods in explainable artificial intelligence and future prospects for integrative physiology.Pflugers Archiv : European journal of physiology · 2025Review
- Machine learning and personalized nutrition: a promising liaison?European journal of clinical nutrition · 2024Article
- Statistical analysis of high-dimensional biomedical data: a gentle introduction to analytical goals, common approaches and challenges.BMC medicine · 2023Review
- 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
- Collective genomic segments with differential pleiotropic patterns between cognitive dimensions and psychopathology.Nature communications · 2022Article
- Machine learning in prediction of genetic risk of nonsyndromic oral clefts in the Brazilian population.Clinical oral investigations · 2021Article
- Introduction to statistical simulations in health research.BMJ open · 2020Article
- Special issue on 'Genetic epidemiology of complex diseases: impact of population history and modelling assumptions'.Human genetics · 2020Article
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
Abstract
In this paper, we give an overview of methodological issues related to the use of statistical learning approaches when analyzing high-dimensional genetic data. The focus is set on regression models and machine learning algorithms taking genetic variables as input and returning a classification or a prediction for the target variable of interest; for example, the present or future disease status, or the future course of a disease. After briefly explaining the basic motivation and principle of these methods, we review different procedures that can be used to evaluate the accuracy of the obtained models and discuss common flaws that may lead to over-optimistic conclusions with respect to their prediction performance and usefulness.
Indexed as
Identifiers
31049651What 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.