Bayesian classifiers for predicting the outcome of breast cancer preoperative chemotherapy

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

Conference ProceedingsOPEN ACCESS

Bayesian classifiers for predicting the outcome of breast cancer preoperative chemotherapy

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2008) 5064 LNAI 263-266

DOI: 10.1007/978-3-540-69939-2_25

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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. © 2008 Springer-Verlag Berlin Heidelberg.

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Braga, A. P., Horta, E. G., Natowicz, R., Rouzier, R., Incitti, R., Rodrigues, T. S., … Çela, A. (2008). Bayesian classifiers for predicting the outcome of breast cancer preoperative chemotherapy. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5064 LNAI, pp. 263–266). Springer Verlag. https://doi.org/10.1007/978-3-540-69939-2_25

Bayesian classifiers for predicting the outcome of breast cancer preoperative chemotherapy

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