title: A simple and efficient algorithm for gene selection using sparse logistic regression.
abstract: BACKGROUND
  This paper gives a new and efficient algorithm for the sparse logistic regression problem. The proposed algorithm is based on the Gauss-Seidel method and is asymptotically convergent. It is simple and extremely easy to implement; it neither uses any sophisticated mathematical programming software nor needs any matrix operations. It can be applied to a variety of real-world problems like identifying marker genes and building a classifier in the context of cancer diagnosis using microarray data.
  
  
  RESULTS
  The gene selection method suggested in this paper is demonstrated on two real-world data sets and the results were found to be consistent with the literature.
  
  
  BACKGROUND
  The implementation of this algorithm is available at the site http://guppy.mpe.nus.edu.sg/~mpessk/SparseLOGREG.shtml
  
  
  BACKGROUND
  Supplementary material is available at the site http://guppy.mpe.nus.edu.sg/~mpessk/SparseLOGREG.shtml
authors: Department of Computer Science and Automation, Indian Institute of Science, Bangalore 560012, India.
authors: S K
authors: SK
authors: Shevade
authors: S S
authors: SS
authors: Keerthi
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000235:genetics; D001943:Breast Neoplasms / Q000145:classification / Q000175:diagnosis / Q000235:genetics; D016000:Cluster Analysis; D003110:Colonic Neoplasms / Q000145:classification / Q000175:diagnosis / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods*; D005820:Genetic Testing / Q000379:methods*; D006801:Humans; D009369:Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D014408:Biomarkers, Tumor