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

Authors:
Vitoantonio Bevilacqua;Paolo Pannarale;Mirko Abbrescia;Claudia Cava;Stefania Tommasi
Affiliations:
Department of Electrical and Electronics, Polytechnic of Bari, Bari, Italy;Department of Electrical and Electronics, Polytechnic of Bari, Bari, Italy;Department of Electrical and Electronics, Polytechnic of Bari, Bari, Italy;Department of Electrical and Electronics, Polytechnic of Bari, Bari, Italy;Istituto Oncologico "Giovanni Paolo II", I.R.C.C.S Ospedale Oncologico di Bari, Bari, Italy
Venue:
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Year:
2011

Citing 2
Cited 0

Gene Selection for Cancer Classification using Support Vector Machines

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Adjustment of systematic microarray data biases

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Abstract

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. Therefore, it is of great interest to integrate different studies thus increasing sample size. In the past, several studies explored the issue of microarray data merging, but the appearance 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. The results showed that breast cancer classification does not take benefit of data merging, confirming the results found by other studies with different techniques.