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

BMC Bioinformatics

Proceedings

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

[1] Department of Electrical and Electronics, Polytechnic of Bari, Via E. Orabona, 4, 70125, Bari, Italy;Istituto Oncologico "Giovanni Paolo II", I.R.C.C.S Ospedale Oncologico di Bari, Viale Orazio Flacco 65, 70124, Bari, Italy;

关键词: Batch Effect;    Recursive Feature Elimination;    Normalize Mutual Information Score;    Batch Effect Removal;    Principal Variance Component Analysis;   

DOI  :  10.1186/1471-2105-13-S7-S9

来源: Springer

【 摘 要 】

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

【 授权许可】

CC BY   
© Bevilacqua et al.; licensee BioMed Central Ltd. 2012