Open Access Open Badges Research

A comparison of machine learning techniques for survival prediction in breast cancer

Leonardo Vanneschi1, Antonella Farinaccio1, Giancarlo Mauri1, Marco Antoniotti1, Paolo Provero23* and Mario Giacobini24*

Author Affiliations

1 Department of Informatics, Systems and Communication (D.I.S.Co.), University of Milano-Bicocca, Milan, Italy

2 Computational Biology Unit, Molecular Biotechnology Center, University of Torino, Italy

3 Department of Genetics, Biology and Biochemistry, University of Torino, Italy

4 Department of Animal Production, Epidemiology and Ecology, University of Torino, Italy

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BioData Mining 2011, 4:12  doi:10.1186/1756-0381-4-12

Published: 11 May 2011



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 Perceptrons and Random Forests in classifying patients from the NKI breast cancer dataset, and comparably to the scoring-based method originally proposed by the authors of the 70-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.