Semi-supervised model applied to the prediction of the response to preoperative chemotherapy for breast cancer

Authors:
Frederico Coelho;Antônio de Pádua Braga;René Natowicz;Roman Rouzier
Affiliations:
PPGEE, CPDEE Universidade Federal de Minas Gerais, Belo Horizonte, Brazil;PPGEE, CPDEE Universidade Federal de Minas Gerais, Belo Horizonte, Brazil;Université Paris-Est, ESIEE-Paris, Département d’ínformatiquex, Paris, France;Hôpital Tenon, Départment of Gynecology, Paris, France
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on Recent advances on machine learning and Cybernetics
Year:
2011

Citing 0
Cited 1

Multi-objective semi-supervised feature selection and model selection based on Pearson's correlation coefficient

CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Breast cancer is the second most frequent one, and the first one affecting the women. The standard treatment has three main stages: a preoperative chemotherapy followed by a surgery operation, then an post-operatory chemotherapy. Because the response to the preoperative chemotherapy is correlated to a good prognosis, and because the clinical and biological information do not yield to efficient predictions of the response, a lot of research effort is being devoted to the design of predictors relying on the measurement of genes’ expression levels. In the present paper, we report our works for designing genomic predictors of the response to the preoperative chemotherapy, making use of a semi-supervised machine learning approach. The method is based on margin geometric information of patterns of low density areas, computed on a labeled dataset and on an unlabeled one.