{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T06:17:00Z","timestamp":1778653020158,"version":"3.51.4"},"reference-count":55,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T00:00:00Z","timestamp":1675814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundations of China","doi-asserted-by":"crossref","award":["32172693"],"award-info":[{"award-number":["32172693"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Tianjin Natural Science Foundation Project","award":["19JCYBJC24800"],"award-info":[{"award-number":["19JCYBJC24800"]}]},{"name":"Program of National Beef Cattle and Yak Industrial Technology System","award":["CARS-37"],"award-info":[{"award-number":["CARS-37"]}]},{"name":"Technology Project of Inner Mongolia Autonomous Region","award":["2020GG0210"],"award-info":[{"award-number":["2020GG0210"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,3,19]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Incorporating the genotypic and phenotypic of the correlated traits into the multi-trait model can significantly improve the prediction accuracy of the target trait in animal and plant breeding, as well as human genetics. However, in most cases, the phenotypic information of the correlated and target trait of the individual to be evaluated was null simultaneously, particularly for the newborn. Therefore, we propose a machine learning framework, MAK, to improve the prediction accuracy of the target trait by constructing the multi-target ensemble regression chains and selecting the assistant trait automatically, which predicted the genomic estimated breeding values of the target trait using genotypic information only. The prediction ability of MAK was significantly more robust than the genomic best linear unbiased prediction, BayesB, BayesRR and the multi trait Bayesian method in the four real animal and plant datasets, and the computational efficiency of MAK was roughly 100 times faster than BayesB and BayesRR.<\/jats:p>","DOI":"10.1093\/bib\/bbad043","type":"journal-article","created":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T12:27:02Z","timestamp":1675859222000},"source":"Crossref","is-referenced-by-count":16,"title":["MAK: a machine learning framework improved genomic prediction via multi-target ensemble regressor chains and automatic selection of assistant traits"],"prefix":"10.1093","volume":"24","author":[{"given":"Mang","family":"Liang","sequence":"first","affiliation":[{"name":"Chinese Academy of Agricultural Sciences Institute of Animal Science"}]},{"given":"Sheng","family":"Cao","sequence":"additional","affiliation":[{"name":"Chinese Academy of Agricultural Sciences Institute of Animal Science"}]},{"given":"Tianyu","family":"Deng","sequence":"additional","affiliation":[{"name":"Chinese Academy of Agricultural Sciences Institute of Animal Science"}]},{"given":"Lili","family":"Du","sequence":"additional","affiliation":[{"name":"Chinese Academy of Agricultural Sciences Institute of Animal Science"}]},{"given":"Keanning","family":"Li","sequence":"additional","affiliation":[{"name":"Chinese Academy of Agricultural Sciences Institute of Animal Science"}]},{"given":"Bingxing","family":"An","sequence":"additional","affiliation":[{"name":"Chinese Academy of Agricultural Sciences Institute of Animal Science"}]},{"given":"Yueying","family":"Du","sequence":"additional","affiliation":[{"name":"Chinese Academy of Agricultural Sciences Institute of Animal Science"}]},{"given":"Lingyang","family":"Xu","sequence":"additional","affiliation":[{"name":"Chinese Academy of Agricultural Sciences Institute of Animal Science"}]},{"given":"Lupei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Chinese Academy of Agricultural Sciences Institute of Animal Science"}]},{"given":"Xue","family":"Gao","sequence":"additional","affiliation":[{"name":"Chinese Academy of Agricultural Sciences Institute of Animal Science"}]},{"given":"Junya","family":"Li","sequence":"additional","affiliation":[{"name":"Chinese Academy of Agricultural Sciences Institute of Animal Science"}]},{"given":"Peng","family":"Guo","sequence":"additional","affiliation":[{"name":"Tianjin Agricultural University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5502-2528","authenticated-orcid":false,"given":"Huijiang","family":"Gao","sequence":"additional","affiliation":[{"name":"Chinese Academy of Agricultural Sciences Institute of Animal Science"}]}],"member":"286","published-online":{"date-parts":[[2023,2,8]]},"reference":[{"issue":"2","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1017\/S0080456800012163","article-title":"XV.\u2014The correlation between relatives on the supposition of Mendelian inheritance","volume":"52","author":"Fisher","year":"1919","journal-title":"Earth Environ Sci Trans R Soc Edinb"},{"issue":"4","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"1819","DOI":"10.1093\/genetics\/157.4.1819","article-title":"Prediction of total genetic value using genome-wide dense marker maps","volume":"157","author":"Meuwissen","year":"2001","journal-title":"Genetics"},{"issue":"2","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"433","DOI":"10.3168\/jds.2008-1646","article-title":"Invited review: genomic selection in dairy cattle: progress and challenges","volume":"92","author":"Hayes","year":"2009","journal-title":"J Dairy Sci"},{"issue":"4","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1111\/j.1439-0388.2006.00595.x","article-title":"Strategy for applying genome-wide selection in dairy cattle","volume":"123","author":"Schaeffer","year":"2006","journal-title":"J Anim Breed Genet"},{"issue":"3","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1007\/s00122-018-3270-8","article-title":"Accelerating crop genetic gains with genomic selection","volume":"132","author":"Voss-Fels","year":"2019","journal-title":"Theor Appl Genet"},{"issue":"6","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"2653","DOI":"10.2527\/jas.2014-8836","article-title":"Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus","volume":"93","author":"Lourenco","year":"2015","journal-title":"J Anim Sci"},{"issue":"1","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"6","DOI":"10.2527\/af.2016-0002","article-title":"Genomic selection: a paradigm shift in animal breeding","volume":"6","author":"Meuwissen","year":"2016","journal-title":"Anim Front"},{"issue":"2","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1038\/ng.1033","article-title":"Genomic and metabolic prediction of complex heterotic traits in hybrid maize","volume":"44","author":"Riedelsheimer","year":"2012","journal-title":"Nat Genet"},{"issue":"2","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"e1004982","DOI":"10.1371\/journal.pgen.1004982","article-title":"Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines","volume":"11","author":"Spindel","year":"2015","journal-title":"PLoS Genet"},{"issue":"1","key":"2023032004460201700_","doi-asserted-by":"crossref","DOI":"10.3835\/plantgenome.2010.12.0029","article-title":"Genomic selection accuracy using multifamily prediction models in a wheat breeding program","volume":"4","author":"Heffner","year":"2011","journal-title":"Plant Genome"},{"issue":"3","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"e20119","DOI":"10.1002\/tpg2.20119","article-title":"Multitrait machine-and deep-learning models for genomic selection using spectral information in a wheat breeding program","volume":"14","author":"Sandhu","year":"2021","journal-title":"Plant Genome"},{"issue":"11","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1016\/j.tplants.2017.08.011","article-title":"Genomic selection in plant breeding: methods, models, and perspectives","volume":"22","author":"Crossa","year":"2017","journal-title":"Trends Plant Sci"},{"issue":"3","key":"2023032004460201700_","doi-asserted-by":"crossref","DOI":"10.3835\/plantgenome2011.08.0024","article-title":"Ridge regression and other kernels for genomic selection with R package rrBLUP","volume":"4","author":"Endelman","year":"2011","journal-title":"Plant Genome"},{"issue":"2","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1534\/genetics.114.164442","article-title":"Genome-wide regression and prediction with the BGLR statistical package","volume":"198","author":"P\u00e9rez","year":"2014","journal-title":"Genetics"},{"issue":"1","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2156-12-87","article-title":"Predicting complex quantitative traits with Bayesian neural networks: a case study with Jersey cows and wheat","volume":"12","author":"Gianola","year":"2011","journal-title":"BMC Genet"},{"issue":"3","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"e20112","DOI":"10.1002\/tpg2.20112","article-title":"The application of pangenomics and machine learning in genomic selection in plants","volume":"14","author":"Bayer","year":"2021","journal-title":"Plant Genome"},{"key":"2023032004460201700_","volume-title":"Machine Learning","author":"Mitchell","year":"1997"},{"key":"2023032004460201700_","first-page":"151","volume-title":"Artificial Intelligence in Design\u201996","author":"Koza","year":"1996"},{"issue":"3","key":"2023032004460201700_","article-title":"A comparison of random forests, boosting and support vector machines for genomic selection","volume":"5","author":"Ogutu","year":"2011","journal-title":"BMC Proc"},{"issue":"7553","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"issue":"2","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"170104","DOI":"10.3835\/plantgenome2017.11.0104","article-title":"Applications of machine learning methods to genomic selection in breeding wheat for rust resistance","volume":"11","author":"Gonz\u00e1lez-Camacho","year":"2018","journal-title":"Plant Genome"},{"issue":"2","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1007\/s10994-019-05848-5","article-title":"An evaluation of machine-learning for predicting phenotype: studies in yeast, rice, and wheat","volume":"109","author":"Grinberg","year":"2020","journal-title":"Mach Learn"},{"issue":"1","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12711-020-00531-z","article-title":"Deep learning versus parametric and ensemble methods for genomic prediction of complex phenotypes","volume":"52","author":"Abdollahi-Arpanahi","year":"2020","journal-title":"Genet Sel Evol"},{"issue":"1","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"e20188","DOI":"10.1002\/tpg2.20188","article-title":"Multi-trait genomic selection can increase selection accuracy for deoxynivalenol accumulation resulting from fusarium head blight in wheat","volume":"15","author":"Gaire","year":"2022","journal-title":"Plant Genome"},{"issue":"1","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-021-02416-w","article-title":"MegaLMM: mega-scale linear mixed models for genomic predictions with thousands of traits","volume":"22","author":"Runcie","year":"2021","journal-title":"Genome Biol"},{"issue":"2","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"491","DOI":"10.2135\/cropsci2018.03.0189","article-title":"Multienvironment and multitrait genomic selection models in unbalanced early-generation wheat yield trials","volume":"59","author":"Ward","year":"2019","journal-title":"Crop Sci"},{"issue":"5","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1002\/widm.1157","article-title":"A survey on multi-output regression","volume":"5","author":"Borchani","year":"2015","journal-title":"WIREs Data Mining Knowl Discov"},{"issue":"5","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.specom.2009.01.001","article-title":"Multi-domain spoken language understanding with transfer learning","volume":"51","author":"Jeong","year":"2009","journal-title":"Speech Commun"},{"issue":"8","key":"2023032004460201700_","article-title":"Multi-target regression with rule ensembles","volume":"13","author":"Aho","year":"2012","journal-title":"J Mach Learn Res"},{"key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"136991","DOI":"10.1016\/j.scitotenv.2020.136991","article-title":"A machine-learning framework for predicting multiple air pollutants' concentrations via multi-target regression and feature selection","volume":"715","author":"Masmoudi","year":"2020","journal-title":"Sci Total Environ"},{"key":"2023032004460201700_","volume-title":"International Conference on Discovery Science","author":"Kocev","year":"2015"},{"issue":"1","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-11-181","article-title":"Multi-task learning for cross-platform siRNA efficacy prediction: an in-silico study","volume":"11","author":"Liu","year":"2010","journal-title":"BMC Bioinform"},{"issue":"21","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"3889","DOI":"10.1093\/bioinformatics\/btab576","article-title":"Predicting correlated outcomes from molecular data","volume":"37","author":"Rauschenberger","year":"2021","journal-title":"Bioinformatics"},{"key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"107027","DOI":"10.1016\/j.cmpb.2022.107027","article-title":"Precise prediction of multiple anticancer drug efficacy using multi target regression and support vector regression analysis","volume":"224","author":"Brindha","year":"2022","journal-title":"Comput Methods Programs Biomed"},{"issue":"3","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1086\/519795","article-title":"PLINK: a tool set for whole-genome association and population-based linkage analyses","volume":"81","author":"Purcell","year":"2007","journal-title":"Am J Hum Genet"},{"issue":"4","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"1503","DOI":"10.1534\/genetics.111.137026","article-title":"Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.)","volume":"190","author":"Resende","year":"2012","journal-title":"Genetics"},{"issue":"3","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1534\/genetics.110.115543","article-title":"Patterns of population structure and environmental associations to aridity across the range of loblolly pine (Pinus taeda L., Pinaceae)","volume":"185","author":"Eckert","year":"2010","journal-title":"Genetics"},{"issue":"1","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/ncomms1467","article-title":"Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa","volume":"2","author":"Zhao","year":"2011","journal-title":"Nat Commun"},{"issue":"1","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1101\/gr.3766306","article-title":"The Oryza bacterial artificial chromosome library resource: construction and analysis of 12 deep-coverage large-insert BAC libraries that represent the 10 genome types of the genus Oryza","volume":"16","author":"Ammiraju","year":"2006","journal-title":"Genome Res"},{"issue":"1","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1105\/tpc.16.00551","article-title":"easyGWAS: a cloud-based platform for comparing the results of genome-wide association studies","volume":"29","author":"Grimm","year":"2017","journal-title":"Plant Cell"},{"issue":"7822","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1038\/s41586-020-2467-6","article-title":"Massive haplotypes underlie ecotypic differentiation in sunflowers","volume":"584","author":"Todesco","year":"2020","journal-title":"Nature"},{"issue":"6","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"bbab132","DOI":"10.1093\/bib\/bbab132","article-title":"KCRR: a nonlinear machine learning with a modified genomic similarity matrix improved the genomic prediction efficiency","volume":"22","author":"An","year":"2021","journal-title":"Brief Bioinform"},{"issue":"2","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1109\/TPAMI.2017.2688363","article-title":"Multi-target regression via robust low-rank learning","volume":"40","author":"Zhen","year":"2017","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s10994-016-5546-z","article-title":"Multi-target regression via input space expansion: treating targets as inputs","volume":"104","author":"Spyromitros-Xioufis","year":"2016","journal-title":"Mach Learni"},{"issue":"11","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"4414","DOI":"10.3168\/jds.2007-0980","article-title":"Efficient methods to compute genomic predictions","volume":"91","author":"VanRaden","year":"2008","journal-title":"J Dairy Sci"},{"key":"2023032004460201700_","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","author":"Dem\u0161ar","year":"2006","journal-title":"J Mach Learn Res"},{"issue":"12","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"2719","DOI":"10.1007\/s00122-018-3186-3","article-title":"Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality","volume":"131","author":"Lado","year":"2018","journal-title":"Theor Appl Genet"},{"issue":"5","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1016\/j.tplants.2018.02.001","article-title":"Translating high-throughput phenotyping into genetic gain","volume":"23","author":"Araus","year":"2018","journal-title":"Trends Plant Sci"},{"key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.3389\/fgene.2019.01197","article-title":"A vision for development and utilization of high-throughput phenotyping and big data analytics in livestock","volume":"10","author":"Koltes","year":"2019","journal-title":"Front Genet"},{"key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1007\/978-3-319-01595-8_18","volume-title":"Data Analysis, Machine Learning and Knowledge Discovery","author":"Senge","year":"2014"},{"key":"2023032004460201700_","article-title":"Rectifying classifier chains for multi-label classification","volume-title":"Lernen, Wissen & Adaptivit\u00e4t, workshopproceedings","author":"Senge"},{"key":"2023032004460201700_","volume-title":"Genetics and Analysis of Quantitative Traits","author":"Lynch","year":"1998"},{"key":"2023032004460201700_","volume-title":"Methodology for Genetic Studies of Twins and Families","author":"Neale","year":"2013"},{"issue":"3","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1007\/s00122-017-3033-y","article-title":"Efficiency of multi-trait, indirect, and trait-assisted genomic selection for improvement of biomass sorghum","volume":"131","author":"Fernandes","year":"2018","journal-title":"Theor Appl Genet"},{"issue":"1","key":"2023032004460201700_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-020-02052-w","article-title":"KAML: improving genomic prediction accuracy of complex traits using machine learning determined parameters","volume":"21","author":"Yin","year":"2020","journal-title":"Genome Biol"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/2\/bbad043\/49560296\/bbad043.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/2\/bbad043\/49560296\/bbad043.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T02:02:08Z","timestamp":1679450528000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbad043\/7031157"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,8]]},"references-count":55,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,3,19]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbad043","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,3]]},"published":{"date-parts":[[2023,2,8]]},"article-number":"bbad043"}}