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Ensemble of Artificial Neural Networks for studying Molecular Markers

Ensembles are well-established as methods for obtaining highly accurate classifiers by combining less accurate ones [1]. An ensemble consists of a set individual predictors that are combined to improve the overall perfomance achieves. Bagging [2] and Boosting [3] are methods frequently used to assemble the ensembles. This work investigates the aplication of Bagging and Boosting, using its version AdaBoostM1 [4] to analyse molecular markers. The use of molecular markers allows the inference of a fenotype from a given genotype [5].This work investigates the use of Articial Neural Networks as individual estimator or in ensembles to analyse the influence of molecular markers in the characteristics of the meat produced in Brazil. It will investigate which markers are more related to the measured fenotypes. The data set used for this work was obtained from the project of Molecular Markers Applied to Meat Production, which was developed at ”Centro de Pesquisas em Pecuária do Sudeste (CPPSEEmbrapa)” under the responsability of Dr. Luciana de Almeida Regitano. The data was obtained from animals generated from the crossing of Nellore cows whith Aberdeen Angus, Canchim, Simmental and Nellore bulls, born in 1998 and 1999. Data of molecular markers of RFLP (Restriction Fragment Lenght Polymorphism)type and microsattelites were obtained, together with production characteristics, such as weight and carcass composition.