Bayesian Classifiers for Predicting the Outcome of Breast Cancer Preoperative Chemotherapy

  • Authors:

  • Antônio P. Braga;Euler G. Horta;René Natowicz;Roman Rouzier;Roberto Incitti;Thiago S. Rodrigues;Marcelo A. Costa;Carmen D. Pataro;Arben Çela

  • Affiliations:

  • Depto. Engenharia Eletrônica, Universidade Federal de Minas Gerais, Brazil;Depto. Engenharia Eletrônica, Universidade Federal de Minas Gerais, Brazil;Université Paris-Est, ESIEE-Paris, France;Hôpital Tenon, Service de gynécologie, France;Institut Mondor de Médecine Moléculaire, Plate-forme génomique, France;Depto. Ciência da Computação, Universidade Federal de Lavras, Brazil;Depto. Engenharia Eletrônica, Universidade Federal de Minas Gerais, Brazil;Depto. Engenharia Eletrônica, Universidade Federal de Minas Gerais, Brazil;Université Paris-Est, ESIEE-Paris, France

  • Venue:
  • ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
  • Year:
  • 2008

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Abstract

Efficient predictors of the response to chemotherapy is an important issue because such predictors would make it possible to give the patients the most appropriate chemotherapy regimen. DNA microarrays appear to be of high interest for the design of such predictors. In this article we propose bayesian classifiers taking as input the expression levels of DNA probes, and a `filtering' method for DNA probes selection.