title: Comparison of data-merging methods with SVM attribute selection and classification in breast cancer gene expression.
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.
authors.affiliation: Department of Electrical and Electronics, Polytechnic of Bari, Via E, Orabona, 4, 70125 Bari, Italy. bevilacqua@poliba.it
authors.forename: Vitoantonio
authors.initials: V
authors.lastname: Bevilacqua
authors.forename: Paolo
authors.initials: P
authors.lastname: Pannarale
authors.forename: Mirko
authors.initials: M
authors.lastname: Abbrescia
authors.forename: Claudia
authors.initials: C
authors.lastname: Cava
authors.forename: Angelo
authors.initials: A
authors.lastname: Paradiso
authors.forename: Stefania
authors.initials: S
authors.lastname: Tommasi
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics*; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D009362:Neoplasm Metastasis / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis; D060388:Support Vector Machine