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

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

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

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.

Original language	English (US)
Title of host publication	Artificial Neural Networks in Pattern Recognition - Third IAPR Workshop, ANNPR 2008, Proceedings
Publisher	Springer Verlag
Pages	263-266
Number of pages	4
ISBN (Print)	3540699384, 9783540699385
DOIs	https://doi.org/10.1007/978-3-540-69939-2_25
State	Published - 2008
Externally published	Yes
Event	3rd IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2008 - Paris, France Duration: Jul 2 2008 → Jul 4 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5064 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	3rd IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2008
Country/Territory	France
City	Paris
Period	07/2/08 → 07/4/08

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

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

Cite this

Braga, A. P., Horta, E. G., Natowicz, R., Rouzier, R., Incitti, R., Rodrigues, T. S., Costa, M. A., Pataro, C. D. M., & Çela, A. (2008). Bayesian classifiers for predicting the outcome of breast cancer preoperative chemotherapy. In Artificial Neural Networks in Pattern Recognition - Third IAPR Workshop, ANNPR 2008, Proceedings (pp. 263-266). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5064 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-540-69939-2_25

Braga, Antônio P. ; Horta, Euler G. ; Natowicz, René et al. / Bayesian classifiers for predicting the outcome of breast cancer preoperative chemotherapy. Artificial Neural Networks in Pattern Recognition - Third IAPR Workshop, ANNPR 2008, Proceedings. Springer Verlag, 2008. pp. 263-266 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{a1bf14cb661643c388dfe1ceb81230d8,

title = "Bayesian classifiers for predicting the outcome of breast cancer preoperative chemotherapy",

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.",

author = "Braga, {Ant{\^o}nio P.} and Horta, {Euler G.} and Ren{\'e} Natowicz and Roman Rouzier and Roberto Incitti and Rodrigues, {Thiago S.} and Costa, {Marcelo A.} and Pataro, {Carmen D.M.} and Arben {\c C}ela",

year = "2008",

doi = "10.1007/978-3-540-69939-2_25",

language = "English (US)",

isbn = "3540699384",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "263--266",

booktitle = "Artificial Neural Networks in Pattern Recognition - Third IAPR Workshop, ANNPR 2008, Proceedings",

address = "Germany",

note = "3rd IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2008 ; Conference date: 02-07-2008 Through 04-07-2008",

}

Braga, AP, Horta, EG, Natowicz, R, Rouzier, R, Incitti, R, Rodrigues, TS, Costa, MA, Pataro, CDM & Çela, A 2008, Bayesian classifiers for predicting the outcome of breast cancer preoperative chemotherapy. in Artificial Neural Networks in Pattern Recognition - Third IAPR Workshop, ANNPR 2008, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5064 LNAI, Springer Verlag, pp. 263-266, 3rd IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2008, Paris, France, 07/2/08. https://doi.org/10.1007/978-3-540-69939-2_25

Bayesian classifiers for predicting the outcome of breast cancer preoperative chemotherapy. / Braga, Antônio P.; Horta, Euler G.; Natowicz, René et al.
Artificial Neural Networks in Pattern Recognition - Third IAPR Workshop, ANNPR 2008, Proceedings. Springer Verlag, 2008. p. 263-266 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5064 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Bayesian classifiers for predicting the outcome of breast cancer preoperative chemotherapy

AU - Braga, Antônio P.

AU - Horta, Euler G.

AU - Natowicz, René

AU - Rouzier, Roman

AU - Incitti, Roberto

AU - Rodrigues, Thiago S.

AU - Costa, Marcelo A.

AU - Pataro, Carmen D.M.

AU - Çela, Arben

PY - 2008

Y1 - 2008

N2 - 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.

AB - 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.

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U2 - 10.1007/978-3-540-69939-2_25

DO - 10.1007/978-3-540-69939-2_25

M3 - Conference contribution

AN - SCOPUS:54249092828

SN - 3540699384

SN - 9783540699385

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 263

EP - 266

BT - Artificial Neural Networks in Pattern Recognition - Third IAPR Workshop, ANNPR 2008, Proceedings

PB - Springer Verlag

T2 - 3rd IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2008

Y2 - 2 July 2008 through 4 July 2008

ER -

Braga AP, Horta EG, Natowicz R, Rouzier R, Incitti R, Rodrigues TS et al. Bayesian classifiers for predicting the outcome of breast cancer preoperative chemotherapy. In Artificial Neural Networks in Pattern Recognition - Third IAPR Workshop, ANNPR 2008, Proceedings. Springer Verlag. 2008. p. 263-266. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-69939-2_25

Bayesian classifiers for predicting the outcome of breast cancer preoperative chemotherapy

Abstract

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ASJC Scopus subject areas

UN SDGs

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