Cluster Analysis and Artificial Neural Network on the Superovulatory Response Prediction in Mice

Gabriela Berni Brianezi, Fernando Frei, José Celso Rocha, Marcelo Fábio Gouveia Nogueira

2012

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.

References

  1. Braga, A. P.; Ludemir, T. B. Redes Neurais Artificiais - Teoria e Aplicações (2nd ed.). Rio de Janeiro: LTC (2007).
  2. Bryson, A. E.; Ho, Y-C. Applied optimal control: optimization, estimation and control. Blaisdell Publishing Company (1969).
  3. Damy, S. B. et al. Aspectos fundamentais da experimentação animal - aplicações em cirurgia experimental. Rev Assoc Med Bras, (2010) 56(1): 103-11.
  4. Everitt, B. S. (1993). Cluster Analysis (3rd. ed.). New York: John Wiley & Son.
  5. Faeder, R. J. Toward a comprehensive language for biological systems. BMC Biology, (2011) 9:68. doi:10.1186/1741-7007-9-68.
  6. Frei, F. Introdução à Análise de Agrupamentos: Teoria e Prática. Sao Paulo State: Editora fundação UNESP (2006).
  7. Haykin, S. Neural Networks. New York: MacMillan College Publishing Company (1994).
  8. Kaufman, L.; Rousseeuw P. J. Finding groups in data: An introduction to cluster analysis. New York: John Wiley & Sons (1990).
  9. Kovács, Z. L. Redes neurais artificiais: Fundamentos e Aplicações (3rd ed.). São Paulo: Livraria da Física Editora (2002).
  10. The Perceptron: A probabilistic model for information storage and organization in the brain. Psychol. Rev, (1958) 65: 386-408.
  11. G. Embryo Development and Morphometry in the The Blue King Crab Paralithodes Platypus Studied by Using Image and Cluster Analysis. Journal of Shellfish Research, (2006) 25(2), 569-576.
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Paper Citation


in Harvard Style

Berni Brianezi G., Frei F., Rocha J. and Fábio Gouveia Nogueira M. (2012). Cluster Analysis and Artificial Neural Network on the Superovulatory Response Prediction in Mice . In Proceedings of the International Workshop on Veterinary Biosignals and Biodevices - Volume 1: VBB, (BIOSTEC 2012) ISBN 978-989-8425-94-2, pages 79-84. DOI: 10.5220/0003876600790084


in Bibtex Style

@conference{vbb12,
author={Gabriela Berni Brianezi and Fernando Frei and José Celso Rocha and Marcelo Fábio Gouveia Nogueira},
title={Cluster Analysis and Artificial Neural Network on the Superovulatory Response Prediction in Mice},
booktitle={Proceedings of the International Workshop on Veterinary Biosignals and Biodevices - Volume 1: VBB, (BIOSTEC 2012)},
year={2012},
pages={79-84},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003876600790084},
isbn={978-989-8425-94-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Workshop on Veterinary Biosignals and Biodevices - Volume 1: VBB, (BIOSTEC 2012)
TI - Cluster Analysis and Artificial Neural Network on the Superovulatory Response Prediction in Mice
SN - 978-989-8425-94-2
AU - Berni Brianezi G.
AU - Frei F.
AU - Rocha J.
AU - Fábio Gouveia Nogueira M.
PY - 2012
SP - 79
EP - 84
DO - 10.5220/0003876600790084