[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120769-en":3,"doc-seo-120769-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120769,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","A review of machine learning models applied to genomic prediction in animal breeding","Modern genotyping technologies have transformed genomic selection in animal breeding, yet traditional genomic prediction methods face limitations in flexibility, accuracy, and computational efficiency when handling large marker datasets. Machine learning approaches have attracted growing interest because they better capture complex patterns in large, noisy data. This review surveys major machine learning algorithms and evaluates their use in genomic estimated breeding values estimation, genotype imputation, and feature selection. Evidence shows strong performance in fitting noisy datasets and modeling some non-additive effects, while results remain context-dependent.","Edinburgh Research Explorer  \nA review of machine learning models applied to genomic prediction in animal breeding  \nCitation for published version:  \nChafai, N, Hayah, I, Houaga, I & Badaoui, B 2023, 'A review of machine learning models applied to genomic prediction in animal breeding', Frontiers in genetics, pp. 1-18. [https://doi.org/10.3389/fgene.2023.1150596](https://doi.org/10.3389/fgene.2023.1150596)  \nDigital Object Identifier (DOI):  \n10.3389/fgene.2023.1150596  \nLink:  \nLink to publication record in Edinburgh Research Explorer  \nDocument Version:  \nPublisher's PDF, also known as Version of record  \nPublished In:  \nFrontiers in genetics  \nGeneral rights  \nCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please [contact openaccess@ed.ac.uk](contact openaccess@ed.ac.uk) providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 21. Sep. 2023  \nTYPE Review  \nPUBLISHED 06 September 2023 DOI 10.3389/fgene.2023.1150596  \nOPEN ACCESS  \nEDITED BY  \nJoanna Szyda,  \nWroclaw University of Environmental and Life Sciences, Poland  \nREVIEWED BY  \nJuliana Petrini,  \nInstituto Clínica do Leite, Brazil Radovan Kasarda,  \nSlovak University of Agriculture, Slovakia  \n*CORRESPONDENCE  \nBouabid Badaoui,  \n [bouabidbadaoui@gmail.com](bouabidbadaoui@gmail.com)  \nRECEIVED 24 January 2023  \nACCEPTED 22 August 2023  \nPUBLISHED 06 September 2023  \nCITATION  \nChafai N, Hayah I, Houaga I and Badaoui B (2023), A review of machine learning models applied to genomic prediction in animal breeding.  \nFront. Genet. 14:1150596 .  \ndoi: 10.3389/fgene.2023.1150596  \nCOPYRIGHT  \n© 2023 Chafai, Hayah, Houaga and Badaoui. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nA review of machine learning models applied to genomic prediction in animal breeding  \nNarjice Chafai 1, Ichrak Hayah 1, Isidore Houaga 2,3 and Bouabid Badaoui 1,4*  \n1Laboratory of Biodiversity, Ecology, and Genome, Department of Biology, Faculty of Sciences, Mohammed V University in Rabat, Rabat, Morocco, 2Centre for Tropical Livestock Genetics and Health, The Roslin Institute, Royal (Dick) School of Veterinary Medicine, The University of Edinburgh, Edinburgh, United Kingdom, 3The Roslin Institute, Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom, 4African Sustainable Agriculture Research Institute (ASARI), Mohammed VI Polytechnic University (UM6P), Laayoune, Morocco  \nThe advent of modern genotyping technologies has revolutionized genomic selection in animal breeding. Large marker datasets have shown several drawbacks for traditional genomic prediction methods in terms of flexibility, accuracy, and computational power. Recently, the application of machine learning models in animal breeding has gained a lot of interest due to their tremendous flexibility and their ability to capture patterns in large noisy datasets. Here, we present a general overview of a handful of machine learning algorithms and their application in genomic prediction to provide a meta-picture of their performance in genomic estimated breeding values estimation, geno","cbCaifc7GPybfLiQ","https://ap.wps.com/l/cbCaifc7GPybfLiQ","pdf",1395637,1,19,"English","en",105,"# Introduction\n## Genomic selection background\n# Machine learning in genomic prediction\n## Algorithms overview\n## Genomic estimated breeding values\n## Genotype imputation\n## Feature selection\n# Comparative performance and limitations\n## When conventional methods outperform\n## Adoption barriers in developing countries\n# Conclusion","[{\"question\":\"Why are machine learning models increasingly studied for genomic prediction in animal breeding?\",\"answer\":\"They offer greater flexibility and can capture patterns in large, noisy marker datasets better than many traditional approaches.\"},{\"question\":\"In which tasks are machine learning models applied for genomic prediction according to the review?\",\"answer\":\"They are used for genomic estimated breeding values estimation, genotype imputation, and feature selection.\"},{\"question\":\"Do machine learning models always outperform conventional genomic prediction methods?\",\"answer\":\"No. Some reviewed studies show that conventional methods can outperform machine learning, indicating there is no universal best method.\"}]","A review of machine learning models applied to genomic prediction in animal breeding | 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