title: Robust biomarker identification for cancer diagnosis with ensemble feature selection methods.
abstract: BACKGROUND
  Biomarker discovery is an important topic in biomedical applications of computational biology, including applications such as gene and SNP selection from high-dimensional data. Surprisingly, the stability with respect to sampling variation or robustness of such selection processes has received attention only recently. However, robustness of biomarkers is an important issue, as it may greatly influence subsequent biological validations. In addition, a more robust set of markers may strengthen the confidence of an expert in the results of a selection method.
  
  
  RESULTS
  Our first contribution is a general framework for the analysis of the robustness of a biomarker selection algorithm. Secondly, we conducted a large-scale analysis of the recently introduced concept of ensemble feature selection, where multiple feature selections are combined in order to increase the robustness of the final set of selected features. We focus on selection methods that are embedded in the estimation of support vector machines (SVMs). SVMs are powerful classification models that have shown state-of-the-art performance on several diagnosis and prognosis tasks on biological data. Their feature selection extensions also offered good results for gene selection tasks. We show that the robustness of SVMs for biomarker discovery can be substantially increased by using ensemble feature selection techniques, while at the same time improving upon classification performances. The proposed methodology is evaluated on four microarray datasets showing increases of up to almost 30% in robustness of the selected biomarkers, along with an improvement of approximately 15% in classification performance. The stability improvement with ensemble methods is particularly noticeable for small signature sizes (a few tens of genes), which is most relevant for the design of a diagnosis or prognosis model from a gene signature.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors: Department of Plant Systems Biology, VIB, Technologiepark 927, 9052 Gent, Belgium.
authors: Thomas
authors: T
authors: Abeel
authors: Thibault
authors: T
authors: Helleputte
authors: Yves
authors: Y
authors: Van de Peer
authors: Pierre
authors: P
authors: Dupont
authors: Yvan
authors: Y
authors: Saeys
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000737:chemistry*; D019295:Computational Biology / Q000379:methods*; D009363:Neoplasm Proteins / Q000032:analysis; D009369:Neoplasms / Q000175:diagnosis*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins