International Journal of Veterinary Sciences and Animal Husbandry
Vol. 5, Issue 3, Part A (2020)
Application of artificial neural network (ann) to evaluate extend of non-linearity among explanatory variables within and between genotypes and phenotypes
Author(s): Sumukwo Chesang, Thomas Kainga Muasya and Kiplangat Ngeno
Abstract: Artificial neural networks (ANN) have been described as an additional model for marker-based genomic predictions of complex traits in the field of animal breeding. This is because of their ability to accommodate noisy, nonlinearity in data set and they make decisions based on prior knowledge. The focus of this study was, therefore, to evaluate the extent of non-linearity among explanatory variables within and between genotypes and phenotypes using ANN. A feedforward ANN was adopted with different number of neurons where Levenberg-Marquardt back-propagation algorithm was used to train the network. The construction and training of the network were done with matrix laboratory (MATLAB). Mean absolute error (MAE) and Pearson’s correlation coefficients (R) were used to measure the ANN predictive performance as a measure of extent of non-linearity among explanatory variables within and between genotypes and phenotypes. The results showed that the ANN models differed in predictive performance depending on the number of neurons in the hidden layer, for instance the neural network with one hidden layer containing 10 neurons in the hidden layer yielded high R-value of 0.86 and MAE of 2.98E-3. When the network dimension was increased to 16 neurons the performance decreased to 0.67 for R and MAE increased to 7.73E-2. After a further increase of neurons to 32 the model yielded R value of 0.27 and MAE of 7.60E-2. The benchmark model for this study had an R of 0.77 and MAE of 5.72. These results imply that a model with 10 neurons is enough to handle nonlinearity in data set thus chosen as the best nonlinear model. This is because dimension reduction of neurons in the hidden layer led to higher, more accurate and more consistent predictions for growth rate. In comparison to linear model, the best nonlinear model performed better though the more complex non-linear architectures with 16 and 32 neurons could not outperform the linear ANN. Thus, linear models can as well produce reliable results for making genomic predictions.
How to cite this article:
Sumukwo Chesang, Thomas Kainga Muasya, Kiplangat Ngeno. Application of artificial neural network (ann) to evaluate extend of non-linearity among explanatory variables within and between genotypes and phenotypes. Int J Vet Sci Anim Husbandry 2020;5(3):21-26.