Belyadi H, Haghighat A (2021). Machine learning guide for oil and gas using Python: A step-by-step breakdown with data, algorithms, codes, and applications. Gulf Prof Pub, pp.169–295.
Ben-Hur A, Horn D, Siegelmann HT, Vapnik V (2001) Support vector clustering. J Mach Learn Res 2, 125-137.
Bobbo T, Biffani S, Taccioli C, et al. (2021) Comparison of machine learning methods to predict udder health status based on somatic cell counts in dairy cows. Sci Rep 11, e13642.
Breiman L (1996) Bagging predictors. Mach Learn 24, 123-140.
Breiman L (2001) Random forests. Mach Learn 45(1), 5-32.
Brownlee J (2016) Bagging and random forest ensemble algorithms for machine learning. Mach Learn Algorithms 133.
Bühlmann P (2012) Bagging, boosting and ensemble methods. Handb Comput Statistics Concepts methods, 985-1022.
Cabrera VE, Barrientos-Blanco JA, Delgado H, Fadul-Pacheco L (2020) Symposium review: Real-time continuous decision making using big data on dairy farms. J Dairy Sci 103, e3856e3866.
Chafai N, Hayah I, Houaga I, Badaoui B (2023) A review of machine learning models applied to genomic prediction in animal breeding. Front Genet 14, e1150596.
Choudhary R, Gianey HK (2017) Comprehensive review on supervised machine learning algorithms. 2017 International Conference on Machine Learning and Data Science (MLDS). IEEE, 37-43.
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20, 273-297.
Crisci C, Ghattas B, Perera G (2012) A review of supervised machine learning algorithms and their applications to ecological data. Ecol Model 240, 113-122.
Crossa J, Montesinos-Lopez OA, Costa-Neto G, et al. (2024) Machine learning algorithms translate big data into predictive breeding accuracy. Trends Plant Sci 24, S1360-1385.
Defazio A, Bach F, Lacoste-Julien S (2014) SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives. Adv Neural Inf Process Syst 27.
Ericsson Unnerstad H, Lindberg A, Persson Waller K, et al. (2009). Microbial aetiology of acute clinical mastitis and agent-specific risk factors. Vet Microbiol 137, e90e97.
Exterkate P, Groenen PJF, Heij C, van Dijk D (2016) Nonlinear forecasting with many predictors using kernel ridge regression. Int J Forecast 32(3), 736-53.
Fadul-Pacheco L, Delgado H, Cabrera VE (2021) Exploring machine learning algorithms for early prediction of clinical mastitis. Int Dairy J 119, e105051.
Forni S, Aguilar I, Misztal I (2011) Different genomic relationship matrices for single-step analysis using phenotypic, pedigree and genomic information. Genet Sel Evol 43, e1.
Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. ICML: Proceedings of the Thirteenth International Conference on Machine Learning 96, 148-156.
Ghotbaldini H, Mohammadabadi MR, Nezamabadi-pour H, et al. (2019) Predicting breeding value of body weight at 6-month age using Artificial Neural Networks in Kermani sheep breed. Acta Scientiarum Anim Sci 41, e45282.
Gianola D, de los Campos G (2018) Inferring genetic values for quantitative traits with regression models. Genetics 208(3), 1391-1404.
Gianola D, Okut H, Weigel KA, Rosa GJ (2011) Predicting complex quantitative traits with bayesian neural networks: A case study with Jersey cows and wheat. BMC Genet 12, 87-14.
González-Camacho JM, Ornella L, Pérez-Rodríguez P, et al. (2018) Applications of machine learning methods to genomic selection in breeding wheat for rust resistance. Plant Genome 11(2), e170104.
Gunn SR (1998) Support vector machines for classification and regression. ISIS Tech Rep 14(1), 5-16.
Jiang T, Gradus JL, Rosellini AJ (2020) Supervised machine learning: A brief primer. Behav Ther 51(5), 675-687.
Kingsford C, Salzberg SL (2008) What are decision trees? Nat Biotechnol 26(9), 1011-1013.
Kotlarz K, Mielczarek M, Biecek P et al. (2024) An Explainable Deep Learning Classifier of Bovine Mastitis Based on Whole-Genome Sequence Data—Circumventing the p >> n Problem. Int J Mol Sci 25, e4715.
LaValley MP (2008) Logistic regression. Circulation 117(18), 2395-2399.
Li X, Chen X, Wang Q, et al. (2024) Integrating bioinformatics and machine learning for genomic prediction in chickens. Genes 15, e690.
Madsen P, Jensen J, Labouriau R, et al. (2014) DMU-A Package for analyzing multivariate mixed models in quantitative genetics and genomics. In: Proceedings of the 10th World Congress of genetics applied to livestock production. August 17-22, Canada.
Mahesh B (2020) Machine learning algorithms-a review. Int J Sci Res 9(1), 381-386.
Manton JH, Amblard PO (2014) A primer on reproducing kernel Hilbert spaces. Available at: http://arxiv.org/abs/1408.0952 (Accessed June 18, 2019).
Maulud D, Abdulazeez AM (2020) A review on linear regression comprehensive in machine learning. J Appl Sci Technol Trends 1(4), 140-147.
Mohammadabadi M, Kheyrodin H, Afanasenko V, et al. (2024) The role of artificial intelligence in genomics. Agric Biotechnol J 16 (2), 195-279.
Montesinos-López OA, Montesinos-López A, Pérez-Rodríguez P, et al. (2021) A review of deep learning applications for genomic selection. BMC Genomics 22, 19-23.
Müller AC, Guido S (2017) Introduction to machine learning with Python: a guide for data scientists. O’Reilly Media, Inc: Sebastopol.
Nasteski V (2017) An overview of the supervised machine learning methods. Horizons B 4, 51-62.
Nayeri S, Sargolzaei M, Tulpan D (2019) A review of traditional and machine learning methods applied to animal breeding. Animal Health Res Rev 20(1), 31-46.
Nick TG, Campbell KM (2007) Logistic regression. Top Biostat 404, 273–301.
Pedregosa F, Varoquaux G, Gramfort A, et al. (2011) Scikit-Learn: Machine Learning in Python. J Mach Learn Res 12, 2825-2830.
Pour Hamidi S, Mohammadabadi MR, Asadi Foozi M, Nezamabadi-pour H (2017) Prediction of breeding values for the milk production trait in Iranian Holstein cows applying artificial neural networks. J Livestock Sci Technol 5(2), 53-61.
Shi S, Li X, Fang L, et al. (2021) Genomic prediction using Bayesian regression models with global-local prior. Front Genet 12, e628205.
Shrestha DL, Solomatine DP (2006) Experiments with AdaBoost.RT, an improved boosting scheme for regression. Neural Comput 18(7), 1678-1710.
Soloshenkov AD, Soloshenkova EA, Semina MT (2024) Artificial intelligence and classical methods in animal genetics and breeding. Russ J Genet 60(7), 843-856.
Vieira S, Pinaya WHL, Garcia-Dias R, Mechelli A (2020) Deep neural networks, in Machine learning (Academic Press), 157-172.
Wang X, Shi S, Wang G, et al. (2022) Using machine learning to improve the accuracy of genomic prediction of reproduction traits in pigs. J Anim Sci Biotechnol 13, e60.
Zhang G, Dai Z, Dai X (2020) C-RNNCrispr: prediction of CRISPR/Cas9 sgRNA activity using convolutional and recurrent neural networks. Comput Struct Biotechnol J 18, 344-354.
Zhang Z (2016) Introduction to machine learning: K-Nearest neighbors. Ann Transl Med 4(11), e218.