Estimadores de Kernel Aplicados na Modelagem e Classificação de Dados de Eficiência de Quimioterapia Neoadjuvante
Resumo
Neste trabalho propomos a aplicação de modelos estatísticos locais ao problema da identificação de pacientes com resposta patológica completa (PCR) para quimioterapia neoadjuvante. A ideia de utilizar modelos locais é particionar o espaço de entrada (com dados de pacientes PCR e NoPCR) e construir um modelo para cada partição. Após a construção dos modelos, foram utilizados classificadores bayesianos e regressão logística para classificar os pacientes em PCR e NoPCR.Referências
Boyle, P. and Levin, B. (2008). World cancer report 2008. Technical report, International Agency for Research on Cancer, Lyon.
Duda, R. O., Hart, P. E., and Stork, D. G. (2000). Pattern Classification. Wiley-Interscience, 2 edition.
Hess, K., Anderson, K., Symmans, W., Valero, V., Ibrahim, N., Mejia, J., Booser, D., Theriault, R., Buzdar, A., Dempsey, P., Rouzier, R., Sneige, N., Ross, J., Vidaurre, T., Gomez, H., Hortobagyi, G., and Pusztai, L. (2006). Pharmacogenomic predictor of sensitivity to preoperative chemotherapy with paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide in breast cancer. Journal of Clinical Oncology, 24(26):4236–4244.
Hosmer, D. W. and Lemeshow, S. (2000). Applied Logistic Regression. Wiley Series in Probability ans Statistics, 2 edition.
Natowicz, R., Braga, A. P., Incitti, R., Horta, E., Rouzier, R., Rodrigues, T. S., and Costa, M. (2008a). A new method of dna probes selection and its use with multi-objective neural networks for predicting the outcome of breast cancer preoperative chemotherapy. In ESANN’2008 proceedings, European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, pages 71–76. d-side publi.
Natowicz, R., Incitti, R., Horta, E. G., Charles, B., Guinot, P., Yan, K., Coutant, C., Andre, F., Pusztai, L., and Rouzier, R. (2008b). Prediction of the outcome of preoperative chemotherapy in breast cancer by dna probes that convey information on both complete and non complete responses. BMC Bioinformatics, 9:149.
Natowicz, R., Incitti, R., Rouzier, R., C¸ ela, A., Braga, Ant o., Horta, E., Rodrigues, T., and Costa, M. (2008c). Downsizing multigenic predictors of the response to preoperative chemotherapy in breast cancer. In KES ’08: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II, pages 157–164, Berlin, Heidelberg. Springer-Verlag.
Silverman, B. (1986). Density estimation for statistics and data analysis. Monographs on Statistics and Applied Probability.
Thompson, J. R. and Tapia, R. A. (1990). Nonparametric function estimation, modeling and simulation. Ed. Siam - Society for Industrial and Applied Mathematics, 1a edition.
Duda, R. O., Hart, P. E., and Stork, D. G. (2000). Pattern Classification. Wiley-Interscience, 2 edition.
Hess, K., Anderson, K., Symmans, W., Valero, V., Ibrahim, N., Mejia, J., Booser, D., Theriault, R., Buzdar, A., Dempsey, P., Rouzier, R., Sneige, N., Ross, J., Vidaurre, T., Gomez, H., Hortobagyi, G., and Pusztai, L. (2006). Pharmacogenomic predictor of sensitivity to preoperative chemotherapy with paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide in breast cancer. Journal of Clinical Oncology, 24(26):4236–4244.
Hosmer, D. W. and Lemeshow, S. (2000). Applied Logistic Regression. Wiley Series in Probability ans Statistics, 2 edition.
Natowicz, R., Braga, A. P., Incitti, R., Horta, E., Rouzier, R., Rodrigues, T. S., and Costa, M. (2008a). A new method of dna probes selection and its use with multi-objective neural networks for predicting the outcome of breast cancer preoperative chemotherapy. In ESANN’2008 proceedings, European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, pages 71–76. d-side publi.
Natowicz, R., Incitti, R., Horta, E. G., Charles, B., Guinot, P., Yan, K., Coutant, C., Andre, F., Pusztai, L., and Rouzier, R. (2008b). Prediction of the outcome of preoperative chemotherapy in breast cancer by dna probes that convey information on both complete and non complete responses. BMC Bioinformatics, 9:149.
Natowicz, R., Incitti, R., Rouzier, R., C¸ ela, A., Braga, Ant o., Horta, E., Rodrigues, T., and Costa, M. (2008c). Downsizing multigenic predictors of the response to preoperative chemotherapy in breast cancer. In KES ’08: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II, pages 157–164, Berlin, Heidelberg. Springer-Verlag.
Silverman, B. (1986). Density estimation for statistics and data analysis. Monographs on Statistics and Applied Probability.
Thompson, J. R. and Tapia, R. A. (1990). Nonparametric function estimation, modeling and simulation. Ed. Siam - Society for Industrial and Applied Mathematics, 1a edition.
Publicado
20/07/2010
Como Citar
WANDERLEY, Maria Fernanda B.; BRAGA, Antônio P.; MENDES, Eduardo M. A. M.; NATOWICZ, René; ROUZIER, Roman.
Estimadores de Kernel Aplicados na Modelagem e Classificação de Dados de Eficiência de Quimioterapia Neoadjuvante. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 10. , 2010, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2010
.
p. 1641-1649.
ISSN 2763-8952.
