Cluster analysis and artificial neural network on the superovulatory response prediction in mice

Abstract

Complex biological systems require sophisticated approach for analysis, once there are variables with distinct measure levels to be analyzed at the same time in them. The mouse assisted reproduction, e.g. superovulation and viable embryos production, demand a multidisciplinary control of the environment, endocrinologic and physiologic status of the animals, of the stressing factors and the conditions which are favorable to their copulation and subsequently oocyte fertilization. In the past, analyses with a simplified approach of these variables were not well succeeded to predict the situations that viable embryos were obtained in mice. Thereby, we suggest a more complex approach with association of the Cluster Analysis and the Artificial Neural Network to predict embryo production in superovulated mice. A robust prediction could avoid the useless death of animals and would allow an ethic management of them in experiments requiring mouse embryo.