Statistical machine-learning methods for genomic prediction using the SKM library

dc.creatorMontesinos-Lopez, O.A.
dc.creatorMosqueda-Gonzalez, B.A.
dc.creatorMontesinos-Lopez, A.
dc.creatorCrossa, J.
dc.date2023-06-22T20:10:11Z
dc.date2023-06-22T20:10:11Z
dc.date2023
dc.date.accessioned2023-07-17T20:10:37Z
dc.date.available2023-07-17T20:10:37Z
dc.identifierhttps://hdl.handle.net/10883/22617
dc.identifier10.3390/genes14051003
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7514360
dc.descriptionGenomic selection (GS) is revolutionizing plant breeding. However, because it is a predictive methodology, a basic understanding of statistical machine-learning methods is necessary for its successful implementation. This methodology uses a reference population that contains both the phenotypic and genotypic information of genotypes to train a statistical machine-learning method. After optimization, this method is used to make predictions of candidate lines for which only genotypic information is available. However, due to a lack of time and appropriate training, it is difficult for breeders and scientists of related fields to learn all the fundamentals of prediction algorithms. With smart or highly automated software, it is possible for these professionals to appropriately implement any state-of-the-art statistical machine-learning method for its collected data without the need for an exhaustive understanding of statistical machine-learning methods and programing. For this reason, we introduce state-of-the-art statistical machine-learning methods using the Sparse Kernel Methods (SKM) R library, with complete guidelines on how to implement seven statistical machine-learning methods that are available in this library for genomic prediction (random forest, Bayesian models, support vector machine, gradient boosted machine, generalized linear models, partial least squares, feed-forward artificial neural networks). This guide includes details of the functions required to implement each of the methods, as well as others for easily implementing different tuning strategies, cross-validation strategies, and metrics to evaluate the prediction performance and different summary functions that compute it. A toy dataset illustrates how to implement statistical machine-learning methods and facilitate their use by professionals who do not possess a strong background in machine learning and programing.
dc.languageEnglish
dc.publisherMDPI
dc.relationhttps://www.mdpi.com/article/10.3390/genes14051003/s1
dc.rightsCIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose
dc.rightsOpen Access
dc.source5
dc.source14
dc.source2073-4425
dc.sourceGenes
dc.source1003
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectSparse Kernel Methods
dc.subjectR package
dc.subjectStatistical Machine Learning
dc.subjectGenomic Selection
dc.subjectMARKER-ASSISTED SELECTION
dc.subjectMACHINE LEARNING
dc.subjectGENOMICS
dc.subjectMETHODS
dc.subjectGenetic Resources
dc.titleStatistical machine-learning methods for genomic prediction using the SKM library
dc.typeArticle
dc.typePublished Version
dc.coverageBasel (Switzerland)


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