[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127417-en":3,"doc-seo-127417-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},127417,962085564807,"Aurelia","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Trends in Plant Science - Feature Review - Expanding genomic prediction in plant breeding - harnessing big data, machine learning, and advanced software","Feature review examining how genomic selection improves genetic gains in plant breeding and what drives its efficiency. Focus centers on statistical machine learning methods and the software that make genomic-enabled prediction accessible. Principles of genomic prediction are connected to big-data workflows, including tools for predicting traits across continuous, binary, categorical, and count phenotypes, and the particular benefits of deep learning models. Also reviews software and data management practices that support adoption of genomic prediction pipelines.","OPEN ACCESS  \nTrends in  \nPlant Science  \nFeature Review  \nExpanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced software  \nJosé Crossa  1 ,2 , Johannes W. R. Martini 3 , Paolo Vitale 1 , Paulino Pérez-Rodríguez 2 , Germano Costa-Neto 4 ,  \nRoberto Fritsche-Neto 5 , Daniel Runcie 6 , Jaime Cuevas 7 , Fernando Toledo 1 , H. Li 1 , Pasquale De Vita 8 , Guillermo Gerard 1 , Susanne Dreisigacker 1 , Leonardo Crespo-Herrera 1 , Carolina Saint Pierre 1 , Alison Bentley 9 , Morten Lillemo 10 , Rodomiro Ortiz  11 , Osval A. Montesinos-López 12 ,* , and Abelardo Montesinos-López 13 ,*  \nWith growing evidence that genomic selection (GS) improves genetic gains in plant breeding, it is timely to review the key factors that improve its effi ciency. In this feature review, we focus on the statistical machine learning (ML) methods and software that are democratizing GS methodology. We outline the principles of genomic-enabled prediction and discuss how statistical ML tools enhance GS efficiency with big data. Additionally, we examine various statistical ML tools developed in recent years for predicting traits across continuous, binary, categorical, and count phenotypes. We highlight the unique advantages of deep learning (DL) models used in genomic prediction (GP) . Finally, we review software developed to democratize the use of GP models and recent data management tools that support the adoption of GS methodology.  \nPredictive plant breeding: a new frontier of genomic innovation  \nThe practical application of genomic tools in plant breeding has opened up a revolutionary era in agriculture, with fundamental changes in how we approach crop improvement. Today, low-cost and high-throughput genotyping technologies allow breeding programs to generate a vast amount of genomic data to support selection decisions and methods such as genome-wide association studies (GWAS) and genomic prediction (GP) (see Glossary) [1] which have become routine in many breeding organizations [2] . Genotypic and phenotypic data characterizing germplasm under varying growing conditions have been accumulated over the years . Moreover, high-throughput phenotyping methods have augmented the number of traits considered, and the extent to which – and resolution at which – phenotypes are measured. With these developments, information technological aspects of breeding, such as data storage and the design of analytical pipelines for knowledge extraction, have moved into the spotlight. Analytical tools need to be able to translate collected data into knowledge, breeding decisions, and ultimately increase genetic gain . A breeding organization ’s ability to leverage information technology will be a key factor in the competition for the most efficient breeding pipelines.  \nOne of the most compelling applications of GP, especially through the integration of multi-omics approaches, is tackling agriculturally important traits characterized by a complex genetic architecture and low heritability, such as ‘yield per hectare ’. These traits are significantly influenced  \nHighlights  \nStatistical machine learning(ML) methods applied to big data and available software have a fundamental role in the democratization of genomic selection (GS) methodology.  \nPrinciples behind genomic-enabled prediction and statistical ML tools can significantly increase the efficiency of the GS methodology under big data.  \nDeep learning (DL) models and methods have been implemented in the context of genomic predictions (GPs), emphasizing the power of this special type of statistical ML tool.  \nThere has been an intense and prolific development of software to be used for the GP models. We provided a brief overview of data management tools generated in recent years to promote the democratization of GS methodology.  \n1International Maize and Wheat Improvement Center (CIMMYT), Carretera México – Veracruz Km. 45, El Batán, CP 56237, Texcoco, Edo. de México, Mexico 2Colegio de","cbCaigsZGAd2oJXz","https://ap.wps.com/l/cbCaigsZGAd2oJXz","pdf",1828911,1,19,"English","en",105,"# Predictive plant breeding: a new frontier of genomic innovation\n## Genomic selection and the role of genomic data\n## From phenotyping to information technology and analytical pipelines\n## Genotypic–environment interaction and low-heritability traits\n# Highlights","[{\"question\":\"What is the main focus of this feature review on genomic selection in plant breeding?\",\"answer\":\"It reviews key factors that improve genomic selection efficiency, emphasizing statistical machine learning methods and software enabling genomic-enabled prediction.\"},{\"question\":\"How do statistical machine learning tools improve genomic prediction under big data?\",\"answer\":\"They use flexible, often non-linear modeling to handle complex datasets and improve trait prediction efficiency for multiple phenotype types.\"},{\"question\":\"Why are deep learning models highlighted in genomic prediction?\",\"answer\":\"The review highlights their unique advantages for genomic prediction, emphasizing the strength of deep learning as a special type of statistical machine learning tool.\"}]","Trends in Plant Science - Feature Review - Expanding genomic prediction in plant breeding - harnessing big data, machine learning, and advanced software | PDF",1785938773,48,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"trends-in-plant-science-feature-review-expanding-genomic-prediction-in-plant-breeding-harnessing-big-data-machine-learning-and-advanced-software","",{"@graph":36,"@context":86},[37,54,69],{"@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/trends-in-plant-science-feature-review-expanding-genomic-prediction-in-plant-breeding-harnessing-big-data-machine-learning-and-advanced-software/127417/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main focus of this feature review on genomic selection in plant breeding?","Question",{"text":76,"@type":77},"It reviews key factors that improve genomic selection efficiency, emphasizing statistical machine learning methods and software enabling genomic-enabled prediction.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How do statistical machine learning tools improve genomic prediction under big data?",{"text":81,"@type":77},"They use flexible, often non-linear modeling to handle complex datasets and improve trait prediction efficiency for multiple phenotype types.",{"name":83,"@type":74,"acceptedAnswer":84},"Why are deep learning models highlighted in genomic prediction?",{"text":85,"@type":77},"The review highlights their unique advantages for genomic prediction, emphasizing the strength of deep learning as a special type of statistical machine learning tool.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},"General","general"]