Artificial Intelligence

Evaluation of the efficiency of artificial neural networks for genetic value prediction

G. N. Silva, Tomaz, R. S., Sant’Anna, I. C., Carneiro, V. Q., Cruz, C. D., Nascimento, M., Silva, G. N., Tomaz, R. S., Sant’Anna, I. C., Carneiro, V. Q., Cruz, C. D., Nascimento, M., Silva, G. N., Tomaz, R. S., Sant’Anna, I. C., Carneiro, V. Q., Cruz, C. D., and Nascimento, M., Evaluation of the efficiency of artificial neural networks for genetic value prediction, vol. 15, p. -, 2016.

Artificial neural networks have shown great potential when applied to breeding programs. In this study, we propose the use of artificial neural networks as a viable alternative to conventional prediction methods. We conduct a thorough evaluation of the efficiency of these networks with respect to the prediction of breeding values. Therefore, we considered eight simulated scenarios, and for the purpose of genetic value prediction, seven statistical parameters in addition to the phenotypic mean in a network designed as a multilayer perceptron.

Superiority of artificial neural networks for a genetic classification procedure

I. C. Sant’Anna, Tomaz, R. S., Silva, G. N., Nascimento, M., Bhering, L. L., and Cruz, C. D., Superiority of artificial neural networks for a genetic classification procedure, vol. 14, pp. 9898-9906, 2015.

The correct classification of individuals is extremely important for the preservation of genetic variability and for maximization of yield in breeding programs using phenotypic traits and genetic markers. The Fisher and Anderson discriminant functions are commonly used multivariate statistical techniques for these situations, which allow for the allocation of an initially unknown individual to predefined groups. However, for higher levels of similarity, such as those found in backcrossed populations, these methods have proven to be inefficient.

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