title: Classification using partial least squares with penalized logistic regression.
abstract: BACKGROUND
  One important aspect of data-mining of microarray data is to discover the molecular variation among cancers. In microarray studies, the number n of samples is relatively small compared to the number p of genes per sample (usually in thousands). It is known that standard statistical methods in classification are efficient (i.e. in the present case, yield successful classifiers) particularly when n is (far) larger than p. This naturally calls for the use of a dimension reduction procedure together with the classification one.
  
  
  RESULTS
  In this paper, the question of classification in such a high-dimensional setting is addressed. We view the classification problem as a regression one with few observations and many predictor variables. We propose a new method combining partial least squares (PLS) and Ridge penalized logistic regression. We review the existing methods based on PLS and/or penalized likelihood techniques, outline their interest in some cases and theoretically explain their sometimes poor behavior. Our procedure is compared with these other classifiers. The predictive performance of the resulting classification rule is illustrated on three data sets: Leukemia, Colon and Prostate.
authors.affiliation: CNRS/LMC-IMAG BP 53, 38041 Grenoble cedex 9, France.
authors.forename: Gersende
authors.initials: G
authors.lastname: Fort
authors.forename: Sophie
authors.initials: S
authors.lastname: Lambert-Lacroix
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000235:genetics / Q000378:metabolism*; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D016018:Least-Squares Analysis; D008957:Models, Genetic; D015233:Models, Statistical; D009363:Neoplasm Proteins / Q000235:genetics / Q000378:metabolism*; D009369:Neoplasms / Q000175:diagnosis / Q000235:genetics* / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins