title: Using multiple regression, Bayesian networks and artificial neural networks for prediction of total egg production in European quails based on earlier expressed phenotypes.
abstract: The prediction of total egg production (TEP) potential in poultry is an important task to aid optimized management decisions in commercial enterprises. The objective of the present study was to compare different modeling approaches for prediction of TEP in meat type quails (Coturnix coturnix coturnix) using phenotypes such as weight, weight gain, egg production and egg quality measurements. Phenotypic data on 30 traits from two lines (L1, n=180; and L2, n=205) of quail were modeled to predict TEP. Prediction models included multiple linear regression and artificial neural network (ANN). Moreover, Bayesian network (BN) and a stepwise approach were used as variable selection methods. BN results showed that TEP is independent from other earlier expressed traits when conditioned on egg production from 35 to 80 days of age (EP1). In addition, the prediction accuracy was much lower when EP1 was not included in the model. The best predictive model was ANN, after feature selection, showing prediction correlations of r=0.792 and r=0.714 for L1 and L2, respectively. In conclusion, machine learning methods may be useful, but reasonable prediction accuracies are obtained only when partial egg production measurements are included in the model.
authors.affiliation: Department of Animal Sciences, University of Wisconsin - Madison, Wisconsin 53706 vfelipe@wisc.edu.
authors.forename: Vivian P S
authors.initials: VP
authors.lastname: Felipe
authors.affiliation: Department of Animal Sciences, Federal University of Jequitinhonha and Mucuri Valleys, Minas Gerais - Brazil.
authors.forename: Martinho A
authors.initials: MA
authors.lastname: Silva
authors.affiliation: Department of Animal Sciences, University of Wisconsin - Madison, Wisconsin 53706.
authors.forename: Bruno D
authors.initials: BD
authors.lastname: Valente
authors.affiliation: Department of Animal Sciences, University of Wisconsin - Madison, Wisconsin 53706.
authors.forename: Guilherme J M
authors.initials: GJ
authors.lastname: Rosa
mesh_terms: D000822:Animal Husbandry / Q000379:methods*; D000818:Animals; D001499:Bayes Theorem; D001938:Brazil; D003370:Coturnix / Q000502:physiology*; D008954:Models, Biological; D016571:Neural Networks, Computer; D012044:Regression Analysis; D012098:Reproduction
keywords: Bayesian networks.; egg production; non-parametric model; phenotypic network; prediction model