{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T15:35:47Z","timestamp":1793028947266,"version":"4.5.0"},"reference-count":81,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T00:00:00Z","timestamp":1792022400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Equine Veterinary Science"],"published-print":{"date-parts":[[2027,1]]},"DOI":"10.1016\/j.jevs.2026.106177","type":"journal-article","created":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T22:37:31Z","timestamp":1792276651000},"page":"106177","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Evaluation of machine learning models for predicting breeding values of key performance traits in Iranian crossbred (Thoroughbred\u202f\u00d7\u202fTurkmen) racehorses"],"prefix":"10.1016","volume":"168","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2142-4841","authenticated-orcid":false,"given":"M.","family":"Taned","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9568-8933","authenticated-orcid":false,"given":"M.B.","family":"Zandi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0270-9625","authenticated-orcid":false,"given":"M.P.","family":"Eskandari Nasab","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6811-9196","authenticated-orcid":false,"given":"A.","family":"Khanteymoori","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.jevs.2026.106177_bib0001","doi-asserted-by":"crossref","DOI":"10.1371\/annotation\/de9e11b9-eb92-4ee5-a56a-908e06d1ed6c","article-title":"A sequence polymorphism in MSTN predicts sprinting ability and racing stamina in thoroughbred horses","volume":"5","author":"Hill","year":"2010","journal-title":"PLoS One"},{"key":"10.1016\/j.jevs.2026.106177_bib0002","series-title":"Rules and regulations for horse racing competitions in Iran","year":"2022"},{"issue":"9","key":"10.1016\/j.jevs.2026.106177_bib0003","doi-asserted-by":"crossref","first-page":"1454","DOI":"10.1071\/AN14677","article-title":"Crossbreed genetic performance study in the eventing horse competition","volume":"56","author":"Cervantes","year":"2016","journal-title":"Anim Prod Sci"},{"key":"10.1016\/j.jevs.2026.106177_bib0004","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.jevs.2016.03.021","article-title":"Genetic parameters for racing performance of thoroughbred horses using Bayesian linear and thurstonian models","volume":"42","author":"da Gama","year":"2016","journal-title":"J Equine Vet Sci"},{"key":"10.1016\/j.jevs.2026.106177_bib0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.jevs.2024.105244","article-title":"Estimation of genetic parameters for racing time and ranking in thoroughbred horses","volume":"144","author":"de Oliveira Padilha","year":"2025","journal-title":"J Equine Vet Sci"},{"key":"10.1016\/j.jevs.2026.106177_bib0006","doi-asserted-by":"crossref","DOI":"10.1016\/j.jevs.2025.105733","article-title":"Genetic parameter estimates of performance traits in Iranian thoroughbred race horses using a bayesian approach","author":"Taned","year":"2025","journal-title":"J Equine Vet Sci"},{"issue":"6","key":"10.1016\/j.jevs.2026.106177_bib0007","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1016\/0301-6226(80)90073-1","article-title":"Heritability of racing ability in Thoroughbreds\u2014A review","volume":"7","author":"Langlois","year":"1980","journal-title":"Livest Prod Sci"},{"key":"10.1016\/j.jevs.2026.106177_bib0008","unstructured":"E.A. Tolley, D.R. Notter, T.J. Marlowe, A review of the inheritance of racing performance in horses, 1985."},{"key":"10.1016\/j.jevs.2026.106177_bib0009","first-page":"961","article-title":"A new way to evaluate race horses, perform","volume":"17","author":"Gillespie","year":"1971","journal-title":"Rate Thoroughbred Rec"},{"issue":"7","key":"10.1016\/j.jevs.2026.106177_bib0010","first-page":"408","article-title":"Performance rates of Thoroughbreds as a criterion of racing ability","volume":"45","author":"Watanabe","year":"1974","journal-title":"Jap J Zootech Sci"},{"issue":"5","key":"10.1016\/j.jevs.2026.106177_bib0011","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1051\/animres:2003033","article-title":"Breeding evaluation of arab horses from their racing results in Tunisia by a BLUP with an animal model approach","volume":"52","author":"Belhajyahia","year":"2003","journal-title":"Anim Res"},{"issue":"6","key":"10.1016\/j.jevs.2026.106177_bib0012","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1051\/animres:2005035","article-title":"Estimation of genetic parameters of thoroughbred racing performance in the Czech Republic","volume":"54","author":"Svobodova","year":"2005","journal-title":"Anim Res"},{"issue":"5","key":"10.1016\/j.jevs.2026.106177_bib0013","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1111\/j.1439-0388.2005.00544.x","article-title":"Phenotypic and genetic parameter estimates for racing traits of Arabian horses in Turkey","volume":"122","author":"Ekiz","year":"2005","journal-title":"J Anim Breed Genet"},{"issue":"1","key":"10.1016\/j.jevs.2026.106177_bib0014","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1038\/s41437-023-00623-8","article-title":"Genetic improvement of speed across distance categories in thoroughbred racehorses in Great Britain","volume":"131","author":"Sharman","year":"2023","journal-title":"Heredity (Edinb)"},{"issue":"19","key":"10.1016\/j.jevs.2026.106177_bib0015","doi-asserted-by":"crossref","first-page":"2630","DOI":"10.3390\/ani12192630","article-title":"Comparison of random regression models with different order Legendre polynomials for genetic parameter estimation on race completion speed of Arabian horses","volume":"12","author":"\u00d6nder","year":"2022","journal-title":"Animals"},{"issue":"12","key":"10.1016\/j.jevs.2026.106177_bib0016","first-page":"2019","article-title":"Racing performance of the quarter horse: genetic parameters, trends and correlation for earnings","volume":"13","author":"Faria","year":"2023","journal-title":"Best Time and Time Class, Animals"},{"key":"10.1016\/j.jevs.2026.106177_bib0017","series-title":"Linear models for the prediction of animal breeding values","first-page":"34","article-title":"Best linear unbiased prediction of breeding value: univariate models with one random effect","author":"Mrode","year":"2014"},{"issue":"1","key":"10.1016\/j.jevs.2026.106177_bib0018","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-022-23499-w","article-title":"Comparison of artificial intelligence algorithms and their ranking for the prediction of genetic merit in sheep","volume":"12","author":"Hamadani","year":"2022","journal-title":"Sci Rep"},{"issue":"4","key":"10.1016\/j.jevs.2026.106177_bib0019","doi-asserted-by":"crossref","DOI":"10.1093\/jas\/skaa101","article-title":"Current status of genomic evaluation","volume":"98","author":"Misztal","year":"2020","journal-title":"J Anim Sci"},{"issue":"6","key":"10.1016\/j.jevs.2026.106177_bib0020","doi-asserted-by":"crossref","first-page":"5141","DOI":"10.3168\/jds.2021-21505","article-title":"Multibreed genomic evaluation for production traits of dairy cattle in the United States using single-step genomic best linear unbiased predictor","volume":"105","author":"Cesarani","year":"2022","journal-title":"J Dairy Sci"},{"key":"10.1016\/j.jevs.2026.106177_bib0021","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1146\/annurev-animal-020420-035235","article-title":"Genetics of thoroughbred racehorse performance","volume":"10","author":"Bailey","year":"2022","journal-title":"Annu Rev Anim Biosci"},{"issue":"4","key":"10.1016\/j.jevs.2026.106177_bib0022","doi-asserted-by":"crossref","first-page":"299","DOI":"10.5194\/aab-66-299-2023","article-title":"Influence of horse demographics, country of training and race distance on the rating of Thoroughbreds","volume":"66","author":"Sobotkov\u00e1","year":"2023","journal-title":"Arch Anim Breed"},{"issue":"6","key":"10.1016\/j.jevs.2026.106177_bib0023","doi-asserted-by":"crossref","first-page":"6174","DOI":"10.3168\/jds.2024-26057","article-title":"Deep learning and genomic best linear unbiased prediction integration: an approach to identify potential nonlinear genetic relationships between traits","volume":"108","author":"Shokor","year":"2025","journal-title":"J Dairy Sci"},{"key":"10.1016\/j.jevs.2026.106177_bib0024","doi-asserted-by":"crossref","DOI":"10.3389\/fgene.2023.1150596","article-title":"A review of machine learning models applied to genomic prediction in animal breeding","volume":"14","author":"Chafai","year":"2023","journal-title":"Front Genet"},{"issue":"2","key":"10.1016\/j.jevs.2026.106177_bib0025","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1007\/s11250-021-02763-7","article-title":"Evaluating advanced computing techniques for predicting breeding values in Harnali sheep","volume":"53","author":"Bangar","year":"2021","journal-title":"Trop Anim Health Prod"},{"key":"10.1016\/j.jevs.2026.106177_bib0026","article-title":"Artificial intelligence in veterinary and animal science: applications, challenges, and future prospects","volume":"235","author":"Hossein-Zadeh","year":"2025","journal-title":"Comput Electron Agric"},{"issue":"5","key":"10.1016\/j.jevs.2026.106177_bib0027","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/s11250-025-04495-4","article-title":"Predicting genetic merit in Harnali sheep using machine learning techniques","volume":"57","author":"Dash","year":"2025","journal-title":"Trop Anim Health Prod"},{"issue":"18","key":"10.1016\/j.jevs.2026.106177_bib0028","doi-asserted-by":"crossref","first-page":"2723","DOI":"10.3390\/ani14182723","article-title":"Supervised machine learning techniques for breeding value prediction in horses: an example using gait visual scores","volume":"14","author":"Bussiman","year":"2024","journal-title":"Animals"},{"issue":"7","key":"10.1016\/j.jevs.2026.106177_bib0029","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0271340","article-title":"Assessing the utility value of Hucul horses using classification models, based on artificial neural networks","volume":"17","author":"Topczewska","year":"2022","journal-title":"PLoS One"},{"issue":"7","key":"10.1016\/j.jevs.2026.106177_bib0030","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1134\/S1022795424700297","article-title":"Artificial intelligence and classical methods in animal genetics and breeding","volume":"60","author":"Soloshenkov","year":"2024","journal-title":"Russ J Genet"},{"issue":"1","key":"10.1016\/j.jevs.2026.106177_bib0031","doi-asserted-by":"crossref","first-page":"1","DOI":"10.17221\/120\/2020-CJAS","article-title":"The assignment success for 22 horse breeds registered in the Czech Republic: the machine learning perspective","volume":"66","author":"\u0160tohlov\u00e1 Putnov\u00e1","year":"2021","journal-title":"Czech J Anim Sci"},{"issue":"4","key":"10.1016\/j.jevs.2026.106177_bib0032","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1007\/BF03195696","article-title":"Horse breed discrimination using machine learning methods","volume":"50","author":"Bur\u00f3cziov\u00e1","year":"2009","journal-title":"J Appl Genet"},{"issue":"2s","key":"10.1016\/j.jevs.2026.106177_bib0033","first-page":"132","article-title":"Horse race results prediction using machine learning algorithms with feature selection","volume":"12","author":"Gupta","year":"2024","journal-title":"Int J Intell Syst Appl Eng"},{"issue":"5","key":"10.1016\/j.jevs.2026.106177_bib0034","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1007\/s11250-022-03294-5","article-title":"Neural network and Bayesian-based prediction of breeding values in Beetal goat","volume":"54","author":"Magotra","year":"2022","journal-title":"Trop Anim Health Prod"},{"key":"10.1016\/j.jevs.2026.106177_bib0035","unstructured":"H. Wickham, J. Bryan, R packages, O\u2019Reilly Media, Inc., 2023."},{"key":"10.1016\/j.jevs.2026.106177_bib0036","series-title":"Data analysis, in: ggplot2: elegant graphics for data analysis","first-page":"189","author":"Wickham","year":"2016"},{"key":"10.1016\/j.jevs.2026.106177_bib0037","unstructured":"T. Wei, V.R. Simko, Package \u201ccorrplot\u201d: visualization of a correlation Matrix (Version 0.92), R Package Corrplot, R software, 2021."},{"key":"10.1016\/j.jevs.2026.106177_bib0038","unstructured":"R. Lenth, emmeans: estimated marginal means, aka least-Squares means, R Package version 1.8.5, 2023."},{"issue":"332","key":"10.1016\/j.jevs.2026.106177_bib0039","first-page":"333","article-title":"Package \u2018car\u2019, Vienna","volume":"16","author":"Fox","year":"2012","journal-title":"R Foundation for Statistical Computing"},{"key":"10.1016\/j.jevs.2026.106177_bib0040","series-title":"Proc. 8th World Congr. Genet. Appl. Livest. Prod. CD-ROM Commun","first-page":"13","article-title":"CFC: a tool for monitoring genetic diversity","author":"Sargolzaei","year":"2006"},{"issue":"4","key":"10.1016\/j.jevs.2026.106177_bib0041","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1002\/(SICI)1097-4636(199612)32:4<655::AID-JBM19>3.0.CO;2-C","article-title":"Inhibition of T and B cell proliferation by titanium, cobalt, and chromium: role of IL-2 and IL-6","volume":"32","author":"Wang","year":"1996","journal-title":"J Biomed Mater Res"},{"issue":"11","key":"10.1016\/j.jevs.2026.106177_bib0042","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1631\/jzus.2007.B0815","article-title":"WOMBAT: a tool for mixed model analyses in quantitative genetics by restricted maximum likelihood (REML)","volume":"8","author":"Meyer","year":"2007","journal-title":"J Zhejiang Univ Sci B"},{"key":"10.1016\/j.jevs.2026.106177_bib0043","unstructured":"Y. Qiu, J. Mei, G. Guennebaud, J. Niesen, RSpectra: solvers for large-scale eigenvalue and SVD problems, R Package version 0.16-1, 2022."},{"key":"10.1016\/j.jevs.2026.106177_bib0072","series-title":"Introduction to linear regression analysis","author":"Montgomery","year":"2021"},{"key":"10.1016\/j.jevs.2026.106177_bib0073","unstructured":"S. Learn Developers, Scikit-learn documentation, Accessed Jun. 20, 2019."},{"key":"10.1016\/j.jevs.2026.106177_bib0074","series-title":"Classification and regression trees","author":"Breiman","year":"2017"},{"issue":"1","key":"10.1016\/j.jevs.2026.106177_bib0075","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely randomized trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Mach Learn"},{"issue":"1","key":"10.1016\/j.jevs.2026.106177_bib0076","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn"},{"key":"10.1016\/j.jevs.2026.106177_bib0077","series-title":"The top ten algorithms in data mining","first-page":"165","article-title":"kNN: k-nearest neighbors","author":"Steinbach","year":"2009"},{"issue":"3","key":"10.1016\/j.jevs.2026.106177_bib0078","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","article-title":"A tutorial on support vector regression","volume":"14","author":"Smola","year":"2004","journal-title":"Stat Comput"},{"key":"10.1016\/j.jevs.2026.106177_bib0079","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: a gradient boosting machine","author":"Friedman","year":"2001","journal-title":"Ann Stat"},{"key":"10.1016\/j.jevs.2026.106177_bib0046","first-page":"31","article-title":"CatBoost: unbiased boosting with categorical features","author":"Prokhorenkova","year":"2018","journal-title":"Adv Neural Inf Process Syst"},{"key":"10.1016\/j.jevs.2026.106177_bib0080","first-page":"3146","article-title":"Lightgbm: a highly efficient gradient boosting decision tree","volume":"30","author":"Ke","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"10.1016\/j.jevs.2026.106177_bib0081","volume":"1","author":"Goodfellow","year":"2016"},{"key":"10.1016\/j.jevs.2026.106177_bib0044","first-page":"2825","article-title":"Scikit-learn: machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"10.1016\/j.jevs.2026.106177_bib0045","series-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"785","article-title":"Xgboost: a scalable tree boosting system","author":"Chen","year":"2016"},{"issue":"7825","key":"10.1016\/j.jevs.2026.106177_bib0047","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","article-title":"Array programming with NumPy","volume":"585","author":"Harris","year":"2020","journal-title":"Nature"},{"issue":"1","key":"10.1016\/j.jevs.2026.106177_bib0048","first-page":"51","article-title":"Data structures for statistical computing in Python","volume":"445","author":"McKinney","year":"2010","journal-title":"SciPy"},{"issue":"03","key":"10.1016\/j.jevs.2026.106177_bib0049","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: a 2D graphics environment","volume":"9","author":"Hunter","year":"2007","journal-title":"Comput Sci Eng"},{"issue":"60","key":"10.1016\/j.jevs.2026.106177_bib0050","doi-asserted-by":"crossref","first-page":"3021","DOI":"10.21105\/joss.03021","article-title":"Seaborn: statistical data visualization","volume":"6","author":"Waskom","year":"2021","journal-title":"J Open Source Softw"},{"key":"10.1016\/j.jevs.2026.106177_bib0051","first-page":"2951","article-title":"Practical bayesian optimization of machine learning algorithms","volume":"25","author":"Snoek","year":"2012","journal-title":"Adv Neural Inf Process Syst"},{"key":"10.1016\/j.jevs.2026.106177_bib0052","unstructured":"T. Head, M. Kumar, H. Nahrstaedt, G. Louppe, I. Shcherbatyi, Scikit-optimize\/scikit-optimize, Zenodo, 2021."},{"issue":"1","key":"10.1016\/j.jevs.2026.106177_bib0053","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s12711-018-0426-6","article-title":"Semi-parametric estimates of population accuracy and bias of predictions of breeding values and future phenotypes using the LR method","volume":"50","author":"Legarra","year":"2018","journal-title":"Genet Sel Evol"},{"issue":"1\u20132","key":"10.1016\/j.jevs.2026.106177_bib0054","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.livsci.2008.05.007","article-title":"Estimation of genetic parameters of racing performance in Iranian thoroughbred horses","volume":"120","author":"Bakhtiari","year":"2009","journal-title":"Livest Sci"},{"issue":"1","key":"10.1016\/j.jevs.2026.106177_bib0055","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1111\/evj.12233","article-title":"Performance selection for thoroughbreds racing in Hong Kong","volume":"47","author":"Velie","year":"2015","journal-title":"Equine Vet J"},{"issue":"1\u20136","key":"10.1016\/j.jevs.2026.106177_bib0056","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1111\/j.1439-0388.1994.tb00445.x","article-title":"Genetics of racing performance in the Japanese thoroughbred horse: I. Description of the data","volume":"111","author":"Oki","year":"1994","journal-title":"J Anim Breed Genet"},{"issue":"1","key":"10.1016\/j.jevs.2026.106177_bib0057","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1111\/age.12234","article-title":"Heritability of racing performance in the Australian thoroughbred racing population","volume":"46","author":"Velie","year":"2015","journal-title":"Anim Genet"},{"issue":"4","key":"10.1016\/j.jevs.2026.106177_bib0058","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1046\/j.1439-0388.2002.00338.x","article-title":"Genetic parameters of racing performance traits of quarter horses in Brazil","volume":"119","author":"Villela","year":"2002","journal-title":"J Anim Breed Genet"},{"key":"10.1016\/j.jevs.2026.106177_bib0059","doi-asserted-by":"crossref","DOI":"10.1016\/j.livsci.2025.105653","article-title":"Fast across all distances: genetic parameters and selection for racing time in quarter Horses using a random regression model","volume":"293","author":"Franco","year":"2025","journal-title":"Livest Sci"},{"issue":"12","key":"10.1016\/j.jevs.2026.106177_bib0060","doi-asserted-by":"crossref","first-page":"523","DOI":"10.17221\/3973-CJAS","article-title":"Genetic correlations between racing performance at different racing distances in Thoroughbreds and Arab horses","volume":"51","author":"Sobczynska","year":"2006","journal-title":"Czech J Anim Sci"},{"issue":"5","key":"10.1016\/j.jevs.2026.106177_bib0061","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1111\/j.1439-0388.2011.00982.x","article-title":"Heritability estimates for racing performance in Japanese thoroughbred racehorses using linear and non-linear model analyses","volume":"129","author":"Tozaki","year":"2012","journal-title":"J Anim Breed Genet"},{"issue":"5","key":"10.1016\/j.jevs.2026.106177_bib0062","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1111\/j.1439-0388.2004.00459.x","article-title":"Genetic parameters of racing merit of thoroughbred horses in Poland","volume":"121","author":"Sobczynska","year":"2004","journal-title":"J Anim Breed Genet"},{"issue":"4","key":"10.1016\/j.jevs.2026.106177_bib0063","first-page":"233","article-title":"Heritability of racing merit of Arab horses","volume":"21","author":"Sobczynska","year":"2003","journal-title":"Anim Sci Pap Reports"},{"key":"10.1016\/j.jevs.2026.106177_bib0064","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1051\/animres:19940409","article-title":"Genetic analysis of thoroughbred racing performance in Spain","author":"Chico","year":"1994","journal-title":"Annales de Zootechnie"},{"issue":"3","key":"10.1016\/j.jevs.2026.106177_bib0065","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1501\/Vetfak_0000002221","article-title":"Some phenotypic and genetic parameters of racing performance in Arabian horses","volume":"56","author":"K\u00f6seman","year":"2009","journal-title":"Ankara \u00dcniversitesi Vet Fak\u00fcltesi Derg"},{"issue":"1","key":"10.1016\/j.jevs.2026.106177_bib0066","doi-asserted-by":"crossref","DOI":"10.1098\/rsos.190824","article-title":"Automated detection of lameness in sheep using machine learning approaches: novel insights into behavioural differences among lame and non-lame sheep","volume":"7","author":"Kaler","year":"2020","journal-title":"R Soc Open Sci"},{"key":"10.1016\/j.jevs.2026.106177_bib0067","doi-asserted-by":"crossref","DOI":"10.4025\/actascianimsci.v41i1.45282","article-title":"Predicting breeding value of body weight at 6-month age using Artificial Neural Networks in Kermani sheep breed","volume":"41","author":"Ghotbaldini","year":"2019","journal-title":"Acta Sci Anim Sci"},{"issue":"1","key":"10.1016\/j.jevs.2026.106177_bib0068","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-023-40528-4","article-title":"Artificial intelligence algorithm comparison and ranking for weight prediction in sheep","volume":"13","author":"Hamadani","year":"2023","journal-title":"Sci Rep"},{"issue":"2","key":"10.1016\/j.jevs.2026.106177_bib0069","first-page":"53","article-title":"Prediction of breeding values for the milk production trait in Iranian Holstein cows applying artificial neural networks","volume":"5","author":"Pour Hamidi","year":"2017","journal-title":"J Livest Sci Technol"},{"issue":"1","key":"10.1016\/j.jevs.2026.106177_bib0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.psj.2024.104489","article-title":"An investigation of machine learning methods applied to genomic prediction in yellow-feathered broilers","volume":"104","author":"Liu","year":"2025","journal-title":"Poult Sci"},{"issue":"12","key":"10.1016\/j.jevs.2026.106177_bib0071","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0189775","article-title":"Estimation of genomic prediction accuracy from reference populations with varying degrees of relationship","volume":"12","author":"Lee","year":"2017","journal-title":"PLoS One"}],"container-title":["Journal of Equine Veterinary Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0737080626004120?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0737080626004120?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T14:56:51Z","timestamp":1793026611000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0737080626004120"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2027,1]]},"references-count":81,"alternative-id":["S0737080626004120"],"URL":"https:\/\/doi.org\/10.1016\/j.jevs.2026.106177","relation":{},"ISSN":["0737-0806"],"issn-type":[{"value":"0737-0806","type":"print"}],"subject":[],"published":{"date-parts":[[2027,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Evaluation of machine learning models for predicting breeding values of key performance traits in Iranian crossbred (Thoroughbred\u202f\u00d7\u202fTurkmen) racehorses","name":"articletitle","label":"Article Title"},{"value":"Journal of Equine Veterinary Science","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jevs.2026.106177","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"106177"}}