title: Gene selection via the BAHSIC family of algorithms.
abstract: BACKGROUND
  Identifying significant genes among thousands of sequences on a microarray is a central challenge for cancer research in bioinformatics. The ultimate goal is to detect the genes that are involved in disease outbreak and progression. A multitude of methods have been proposed for this task of feature selection, yet the selected gene lists differ greatly between different methods. To accomplish biologically meaningful gene selection from microarray data, we have to understand the theoretical connections and the differences between these methods. In this article, we define a kernel-based framework for feature selection based on the Hilbert-Schmidt independence criterion and backward elimination, called BAHSIC. We show that several well-known feature selectors are instances of BAHSIC, thereby clarifying their relationship. Furthermore, by choosing a different kernel, BAHSIC allows us to easily define novel feature selection algorithms. As a further advantage, feature selection via BAHSIC works directly on multiclass problems.
  
  
  RESULTS
  In a broad experimental evaluation, the members of the BAHSIC family reach high levels of accuracy and robustness when compared to other feature selection techniques. Experiments show that features selected with a linear kernel provide the best classification performance in general, but if strong non-linearities are present in the data then non-linear kernels can be more suitable.
  
  
  BACKGROUND
  Accompanying homepage is http://www.dbs.ifi.lmu.de/~borgward/BAHSIC.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: National ICT Australia and Australian National University, Canberra, Australia.
authors.forename: Le
authors.initials: L
authors.lastname: Song
authors.forename: Justin
authors.initials: J
authors.lastname: Bedo
authors.forename: Karsten M
authors.initials: KM
authors.lastname: Borgwardt
authors.forename: Arthur
authors.initials: A
authors.lastname: Gretton
authors.forename: Alex
authors.initials: A
authors.lastname: Smola
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D009363:Neoplasm Proteins / Q000032:analysis*; D009369:Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D012680:Sensitivity and Specificity
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins