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Supervised Machine Learning Techniques for Breeding Value Prediction in Horses: An Example Using Gait Visual Scores. | LitMetric

AI Article Synopsis

  • * The research used a large dataset of over 5000 records from 107,951 horses over 14 generations, applying multiple-trait models to estimate breeding values and then training various machine learning models, including ANN, RFR, and SVR.
  • * While all machine learning models showed comparable accuracy in predicting breeding values, artificial neural networks had slightly better performance but also higher bias and variability, suggesting they can be useful yet may have limitations, especially for young animals.

Article Abstract

Gait scores are widely used in the genetic evaluation of horses. However, the nature of such measurement may limit genetic progress since there is subjectivity in phenotypic information. This study aimed to assess the application of machine learning techniques in the prediction of breeding values for five visual gait scores in Campolina horses: dissociation, comfort, style, regularity, and development. The dataset contained over 5000 phenotypic records with 107,951 horses (14 generations) in the pedigree. A fixed model was used to estimate least-square solutions for fixed effects and adjusted phenotypes. Variance components and breeding values (EBV) were obtained via a multiple-trait model (MTM). Adjusted phenotypes and fixed effects solutions were used to train machine learning models (using the EBV from MTM as target variable): artificial neural network (ANN), random forest regression (RFR) and support vector regression (SVR). To validate the models, the linear regression method was used. Accuracy was comparable across all models (but it was slightly higher for ANN). The highest bias was observed for ANN, followed by MTM. Dispersion varied according to the trait; it was higher for ANN and the lowest for MTM. Machine learning is a feasible alternative to EBV prediction; however, this method will be slightly biased and over-dispersed for young animals.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11429212PMC
http://dx.doi.org/10.3390/ani14182723DOI Listing

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