Performance of Artificial Neural Networks (ANNs) and linear mixed models for prediction of breeding values

Document Type : Research Paper

Authors

1 Department of Animal Science, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran

2 Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, Ontario, Canada

Abstract

Artificial neural networks (ANN) have been widely used for both prediction and classification tasks in many fields of knowledge; however, few studies are available on animal science. The objective of this study was to prediction of breeding values of weaning weight in Markhoz goats based on the Mixed Model Equation (MME) and Artificial Neural Networks (ANNs). Quality control and calculation of descriptive statistics was performed using the GLM procedure of the SAS statistical package. The pedigree file included 5541 kids produced by 261 bucks and 1616 does. In the first step, genetic evaluations and Best Linear Unbiased Prediction (BLUP) of breeding values for weaning weight was computed with the mixed model equations, animal model by DMU program. Later, unique dataset was introduced to the ANN models by the R statistical program. A variety of models including, multilayer perceptron (MLP), radial basis function (RBF) and Support Vector Regression (SVR) were used to build the neural models. The artificial neural networks were trained and several networks (different hidden layers and nodes/ neurons) were evaluated. In artificial neural networks, the data were randomly divided to two parts (75% training and 25% for test/validation). Best architecture was selected according to the mean square of error and correlation. Correlation between true breeding value and estimated breeding value for MLP, RBF and SVR models were 0.72, 0.49 and 0.73, respectively. Analysis of farm data showed that the MLP and SVR models have higher performance than RBF for prediction of breeding values or ranking of individuals.

Keywords

Main Subjects

Iranian Journal of animal Science

Articles in Press, Accepted Manuscript
Available Online from 19 November 2023

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History

  • Receive Date: 17 February 2023
  • Revise Date: 21 October 2023
  • Accept Date: 30 October 2023

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