[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117880-en":3,"doc-seo-117880-105":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},117880,34359740700684,"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 - review","Modern genotyping technologies have reshaped genomic selection in animal breeding, yet large marker datasets can expose limitations of conventional genomic prediction methods in flexibility, accuracy, and computational efficiency. This review surveys machine learning algorithms and their use in genomic prediction tasks, including genomic estimated breeding values estimation, genotype imputation, and feature selection. Evidence from reviewed studies indicates strong performance for fitting large noisy datasets and modeling certain non-additive effects, while results remain inconsistent across methods, with adoption constrained by data limitations and limited interpretability.","TYPE 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, genotype imputation, and feature selection. Finally, we discuss a potential adoption of machine learning models in genomic prediction in developing countries. The results of the reviewed studies showed that machine learning models have indeed performed well in fitting large noisy data sets and modeling minor nonadditive effects in some of the studies. However, sometimes conventional methods outperformed machine learning models, which confirms that there’s no universal method for genomic prediction. In summary, machine learning models have great potential for extracting patterns from single nucleotide polymorphism datasets. Nonetheless, the level of their adoption in animal breeding is still low due to data limitations, complex genetic interactions, a lack of standardization and reproducibility, and the lack of interpretability of machine learning models when trained with biological data. Consequently, there is no remarkable outperformance of machine learning methods compared to traditional methods in genomic prediction. Therefore, more research should be conducted to discover new insights that could enhance livestock breeding programs.  \nKEYWORDS  \nartificial intelligence, algorithms, classification, regression, genomic selection, animal breeding, SNPs  \nFrontiers in Genetics 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nFarmers and animal breeders have long used","cbCaifR3jOS1w26Z","https://ap.wps.com/l/cbCaifR3jOS1w26Z","pdf",1280468,1,18,"English","en",105,"# Introduction\n## Genomic selection and traditional breeding value estimation\n## Molecular genetics and marker-assisted selection\n## Challenges with low heritability and data requirements\n# Machine learning algorithms in genomic prediction","[{\"question\":\"What is the main focus of this review?\",\"answer\":\"It provides an overview of machine learning models applied to genomic prediction in animal breeding, summarizing their performance across key tasks.\"},{\"question\":\"Which genomic prediction tasks are covered?\",\"answer\":\"The review discusses machine learning applications in genomic estimated breeding values estimation, genotype imputation, and feature selection.\"},{\"question\":\"Do machine learning methods consistently outperform traditional methods?\",\"answer\":\"No. Some studies show advantages for modeling complex effects and fitting noisy data, but conventional approaches can outperform machine learning, indicating no universal best method.\"}]","A review of machine learning models applied to genomic prediction in animal breeding - review | PDF",1785680116,45,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-review-of-machine-learning-models-applied-to-genomic-prediction-in-animal-breeding-review","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-review-of-machine-learning-models-applied-to-genomic-prediction-in-animal-breeding-review/117880/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main focus of this review?","Question",{"text":75,"@type":76},"It provides an overview of machine learning models applied to genomic prediction in animal breeding, summarizing their performance across key tasks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which genomic prediction tasks are covered?",{"text":80,"@type":76},"The review discusses machine learning applications in genomic estimated breeding values estimation, genotype imputation, and feature selection.",{"name":82,"@type":73,"acceptedAnswer":83},"Do machine learning methods consistently outperform traditional methods?",{"text":84,"@type":76},"No. Some studies show advantages for modeling complex effects and fitting noisy data, but conventional approaches can outperform machine learning, indicating no universal best method.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]