ArticleBMC bioinformatics2024
Ensemble learning for integrative prediction of genetic values with genomic variants.
Article in BMC bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 7 citations in OpenAlex.
- Evolutionary structure constrains genomic prediction accuracy more than model complexity in mango (Mangifera indica L.).G3 (Bethesda, Md.) · 2026Article
- Evaluating ensemble learning approaches for horizontal gene transfer detection.Scientific reports · 2026Article
- Ensemble of Bayesian alphabets via constraint weight optimization strategy improves genomic prediction accuracy.G3 (Bethesda, Md.) · 2025Article
- Increased genomic predictive ability in mango using GWAS-preselected variants and fixed-effect SNPs.Frontiers in plant science · 2025Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundWhole genome variants offer sufficient information for genetic prediction of human disease risk, and prediction of animal and plant breeding values. Many sophisticated statistical methods have been developed for enhancing the predictive ability. However, each method has its own advantages and disadvantages, so far, no one method can beat others.
resultsWe herein propose an Ensemble Learning method for Prediction of Genetic Values (ELPGV), which assembles predictions from several basic methods such as GBLUP, BayesA, BayesB and BayesCπ, to produce more accurate predictions. We validated ELPGV with a variety of well-known datasets and a serious of simulated datasets. All revealed that ELPGV was able to significantly enhance the predictive ability than any basic methods, for instance, the comparison p-value of ELPGV over basic methods were varied from 4.853E-118 to 9.640E-20 for WTCCC dataset.
conclusionsELPGV is able to integrate the merit of each method together to produce significantly higher predictive ability than any basic methods and it is simple to implement, fast to run, without using genotype data. is promising for wide application in genetic predictions.
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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.