title: A new regularized least squares support vector regression for gene selection.
abstract: BACKGROUND
  Selection of influential genes with microarray data often faces the difficulties of a large number of genes and a relatively small group of subjects. In addition to the curse of dimensionality, many gene selection methods weight the contribution from each individual subject equally. This equal-contribution assumption cannot account for the possible dependence among subjects who associate similarly to the disease, and may restrict the selection of influential genes.
  
  
  RESULTS
  A novel approach to gene selection is proposed based on kernel similarities and kernel weights. We do not assume uniformity for subject contribution. Weights are calculated via regularized least squares support vector regression (RLS-SVR) of class levels on kernel similarities and are used to weight subject contribution. The cumulative sum of weighted expression levels are next ranked to select responsible genes. These procedures also work for multiclass classification. We demonstrate this algorithm on acute leukemia, colon cancer, small, round blue cell tumors of childhood, breast cancer, and lung cancer studies, using kernel Fisher discriminant analysis and support vector machines as classifiers. Other procedures are compared as well.
  
  
  CONCLUSIONS
  This approach is easy to implement and fast in computation for both binary and multiclass problems. The gene set provided by the RLS-SVR weight-based approach contains a less number of genes, and achieves a higher accuracy than other procedures.
authors.affiliation: 1Bioinformatics and Biostatistics Core Laboratory, National Taiwan University, Taipei, Taiwan, Republic of China. d93842005@ntu.edu.tw
authors.forename: Pei-Chun
authors.initials: PC
authors.lastname: Chen
authors.forename: Su-Yun
authors.initials: SY
authors.lastname: Huang
authors.forename: Wei J
authors.initials: WJ
authors.lastname: Chen
authors.forename: Chuhsing K
authors.initials: CK
authors.lastname: Hsiao
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods; D052138:Genes, Neoplasm; D006801:Humans; D016018:Least-Squares Analysis; D009369:Neoplasms / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods