The ability to accurately classify cancer patients into risk classes, i.e. to predict the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years gene expression data have been successfully used to complement the clinical and histological criteria traditionally used in such prediction. Many "gene expression signatures" have been developed, i.e. sets of genes whose expression values in a tumor can be used to predict the outcome of the pathology. Here we investigate the use of several machine learning techniques to classify breast cancer patients using one of such signatures, the well established 70-gene signature. We show that Genetic Programming performs significantly better than Support Vector Machines, Multilayered Perceptron and Random Forest in classifying patients from the NKI breast cancer dataset, and slightly better than the scoring-based method originally proposed by the authors of the seventy-gene signature. Furthermore, Genetic Programming is able to perform an automatic feature selection. Since the performance of Genetic Programming is likely to be improvable compared to the out-of-the-box approach used here, and given the biological insight potentially provided by the Genetic Programming solutions, we conclude that Genetic Programming methods are worth further investigation as a tool for cancer patient classification based on gene expression data. © 2010 Springer-Verlag Berlin Heidelberg.

Vanneschi, L., Farinaccio, A., Giacobini, M., Antoniotti, M., Mauri, G., & Provero, P. (2010). Identification of Individualized Feature Combinations for Survival Prediction in Breast Cancer: A Comparison of Machine Learning Techniques. In Proceedings of the EvoBIO 2010 Conference, Evo* 2010 (pp.110-121) [10.1007/978-3-642-12211-8_10].

Identification of Individualized Feature Combinations for Survival Prediction in Breast Cancer: A Comparison of Machine Learning Techniques

VANNESCHI, LEONARDO;FARINACCIO, ANTONELLA;ANTONIOTTI, MARCO;MAURI, GIANCARLO;
2010

Abstract

The ability to accurately classify cancer patients into risk classes, i.e. to predict the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years gene expression data have been successfully used to complement the clinical and histological criteria traditionally used in such prediction. Many "gene expression signatures" have been developed, i.e. sets of genes whose expression values in a tumor can be used to predict the outcome of the pathology. Here we investigate the use of several machine learning techniques to classify breast cancer patients using one of such signatures, the well established 70-gene signature. We show that Genetic Programming performs significantly better than Support Vector Machines, Multilayered Perceptron and Random Forest in classifying patients from the NKI breast cancer dataset, and slightly better than the scoring-based method originally proposed by the authors of the seventy-gene signature. Furthermore, Genetic Programming is able to perform an automatic feature selection. Since the performance of Genetic Programming is likely to be improvable compared to the out-of-the-box approach used here, and given the biological insight potentially provided by the Genetic Programming solutions, we conclude that Genetic Programming methods are worth further investigation as a tool for cancer patient classification based on gene expression data. © 2010 Springer-Verlag Berlin Heidelberg.
paper
Machine learning, Cancer Research, Breast Cancer
English
EvoBIO 2010, Evo* 2010
9783642122101
Vanneschi, L., Farinaccio, A., Giacobini, M., Antoniotti, M., Mauri, G., & Provero, P. (2010). Identification of Individualized Feature Combinations for Survival Prediction in Breast Cancer: A Comparison of Machine Learning Techniques. In Proceedings of the EvoBIO 2010 Conference, Evo* 2010 (pp.110-121) [10.1007/978-3-642-12211-8_10].
Vanneschi, L; Farinaccio, A; Giacobini, M; Antoniotti, M; Mauri, G; Provero, P
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/8624
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