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This article is part of the supplement: Advanced intelligent computing theories and their applications in bioinformatics. Proceedings of the 2011 International Conference on Intelligent Computing (ICIC 2011)

Open Access Proceedings

Comparison of data-merging methods with SVM attribute selection and classification in breast cancer gene expression

Vitoantonio Bevilacqua1*, Paolo Pannarale1, Mirko Abbrescia1, Claudia Cava1, Angelo Paradiso2 and Stefania Tommasi2

Author affiliations

1 Department of Electrical and Electronics, Polytechnic of Bari, Via E. Orabona, 4, 70125 Bari, Italy

2 Istituto Oncologico "Giovanni Paolo II", I.R.C.C.S Ospedale Oncologico di Bari, Viale Orazio Flacco 65, 70124 Bari, Italy

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Citation and License

BMC Bioinformatics 2012, 13(Suppl 7):S9  doi:10.1186/1471-2105-13-S7-S9

Published: 8 May 2012

Abstract

Background

DNA microarray data are used to identify genes which could be considered prognostic markers. However, due to the limited sample size of each study, the signatures are unstable in terms of the composing genes and may be limited in terms of performances. It is therefore of great interest to integrate different studies, thus increasing sample size.

Results

In the past, several studies explored the issue of microarray data merging, but the arrival of new techniques and a focus on SVM based classification needed further investigation. We used distant metastasis prediction based on SVM attribute selection and classification to three breast cancer data sets.

Conclusions

The results showed that breast cancer classification does not benefit from data merging, confirming the results found by other studies with different techniques.