ArticleBioinformatics (Oxford, England)2016
Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Impact of scale parameter for marker variance prior in some Bayesian whole-genome regression methods.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Article
- Advances in multi-trait genomic prediction approaches: classification, comparative analysis, and perspectives.Briefings in bioinformatics · 2025Review
- CropGS-Hub: a comprehensive database of genotype and phenotype resources for genomic prediction in major crops.Nucleic acids research · 2024Article
- (Quasi) multitask support vector regression with heuristic hyperparameter optimization for whole-genome prediction of complex traits: a case study with carcass traits in broilers.G3 (Bethesda, Md.) · 2023Article
- Partial least squares enhance multi-trait genomic prediction of potato cultivars in new environments.Scientific reports · 2023Article
- Modeling genotype × environment interaction for single and multitrait genomic prediction in potato (Solanum tuberosum L.).G3 (Bethesda, Md.) · 2023Article
- Bayesian multitrait kernel methods improve multienvironment genome-based prediction.G3 (Bethesda, Md.) · 2022Article
- Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat.Frontiers in genetics · 2022Article
- Accounting for Correlation Between Traits in Genomic Prediction.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Multi-trait genome prediction of new environments with partial least squares.Frontiers in genetics · 2022Article
- L2,1-norm regularized multivariate regression model with applications to genomic prediction.Bioinformatics (Oxford, England) · 2021Article
- Accounting for epistasis improves genomic prediction of phenotypes with univariate and bivariate models across environments.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Article
- Genome-based prediction of multiple wheat quality traits in multiple years.The plant genome · 2020Article
- Development and validation of a simple risk model to predict major cancers for patients with nonalcoholic fatty liver disease.Cancer medicine · 2020Article
- A Bayesian Genomic Multi-output Regressor Stacking Model for Predicting Multi-trait Multi-environment Plant Breeding Data.G3 (Bethesda, Md.) · 2019Article
- An R Package for Bayesian Analysis of Multi-environment and Multi-trait Multi-environment Data for Genome-Based Prediction.G3 (Bethesda, Md.) · 2019Article
- New Deep Learning Genomic-Based Prediction Model for Multiple Traits with Binary, Ordinal, and Continuous Phenotypes.G3 (Bethesda, Md.) · 2019Article
- A singular value decomposition Bayesian multiple-trait and multiple-environment genomic model.Heredity · 2019Article
- Multi-trait, Multi-environment Deep Learning Modeling for Genomic-Enabled Prediction of Plant Traits.G3 (Bethesda, Md.) · 2018Article
- Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2018Article
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3 authors.
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Abstract
unlabelledGiven a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait prediction is usually represented as linear regression models. In many cases, for the same set of samples and markers, multiple traits are observed. Some of these traits might be correlated with each other. Therefore, modeling all the multiple traits together may improve the prediction accuracy. In this work, we view the multitrait prediction problem from a machine learning angle: as either a multitask learning problem or a multiple output regression problem, depending on whether different traits share the same genotype matrix or not. We then adapted multitask learning algorithms and multiple output regression algorithms to solve the multitrait prediction problem. We proposed a few strategies to improve the least square error of the prediction from these algorithms. Our experiments show that modeling multiple traits together could improve the prediction accuracy for correlated traits. AVAILABILITY AND IMPLEMENTATION: The programs we used are either public or directly from the referred authors, such as MALSAR (http://www.public.asu.edu/~jye02/Software/MALSAR/) package. The Avocado data set has not been published yet and is available upon request. CONTACT: dhe@us.ibm.com.
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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.