Using Multiple Regression, Bayesian Networks and Artificial Neural Networks for Prediction of Total Egg Production in European Quails

Abstract Text: Phenotypic data on 30 production traits of 385 quails from two lines were modeled to predict total egg production (TEP). Prediction models included linear regression and artificial neural networks (ANN). Bayesian networks and a stepwise approach were applied as variable selection methods. The learned structures for the two lines show that partial egg production is the only variable in TEP’s Markov Blanket, which implies expected independence from the other traits considered in these sets. Furthermore, even if no causal interpretation is projected on the output, such data-driven analysis is interesting to verify if the statistical consequences of the recovered graph are consistent with prior biological beliefs about the system.  The best predictive model was ANN after feature selection, showing .79 and .71 maximum prediction accuracy for lines 1 and 2, respectively. In conclusion, for prediction of TEP, a partial egg production measurement is necessary.

Keywords: Phenotype prediction, networks