COMBINING GENE EXPRESSION AND CLINICAL DATA TO INCREASE PERFORMANCE OF PROGNOSTIC BREAST CANCER MODELS

Jana Šilhavá, Pavel Smrž

Abstract

Microarray class prediction is an important application of gene expression data in biomedical research. Combining gene expression data with other relevant data may add valuable information and can generate more accurate prognostic predictions. In this paper, we combine gene expression data with clinical data. We use logistic regression models that can be built through various regularized techniques. Generalized linear models enables combining of these models with different structure of data. Our two suggested approaches are evaluated with publicly available breast cancer data sets. Based on the results, our approaches have a positive effect on prediction performances and are not computationally intensive.

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Paper Citation


in Harvard Style

Šilhavá J. and Smrž P. (2012). COMBINING GENE EXPRESSION AND CLINICAL DATA TO INCREASE PERFORMANCE OF PROGNOSTIC BREAST CANCER MODELS . In Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: SSML, (ICAART 2012) ISBN 978-989-8425-95-9, pages 589-594. DOI: 10.5220/0003881505890594


in Bibtex Style

@conference{ssml12,
author={Jana Šilhavá and Pavel Smrž},
title={COMBINING GENE EXPRESSION AND CLINICAL DATA TO INCREASE PERFORMANCE OF PROGNOSTIC BREAST CANCER MODELS},
booktitle={Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: SSML, (ICAART 2012)},
year={2012},
pages={589-594},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003881505890594},
isbn={978-989-8425-95-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: SSML, (ICAART 2012)
TI - COMBINING GENE EXPRESSION AND CLINICAL DATA TO INCREASE PERFORMANCE OF PROGNOSTIC BREAST CANCER MODELS
SN - 978-989-8425-95-9
AU - Šilhavá J.
AU - Smrž P.
PY - 2012
SP - 589
EP - 594
DO - 10.5220/0003881505890594