title: A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis.
abstract: BACKGROUND
  Cancer diagnosis is one of the most important emerging clinical applications of gene expression microarray technology. We are seeking to develop a computer system for powerful and reliable cancer diagnostic model creation based on microarray data. To keep a realistic perspective on clinical applications we focus on multicategory diagnosis. To equip the system with the optimum combination of classifier, gene selection and cross-validation methods, we performed a systematic and comprehensive evaluation of several major algorithms for multicategory classification, several gene selection methods, multiple ensemble classifier methods and two cross-validation designs using 11 datasets spanning 74 diagnostic categories and 41 cancer types and 12 normal tissue types.
  
  
  RESULTS
  Multicategory support vector machines (MC-SVMs) are the most effective classifiers in performing accurate cancer diagnosis from gene expression data. The MC-SVM techniques by Crammer and Singer, Weston and Watkins and one-versus-rest were found to be the best methods in this domain. MC-SVMs outperform other popular machine learning algorithms, such as k-nearest neighbors, backpropagation and probabilistic neural networks, often to a remarkable degree. Gene selection techniques can significantly improve the classification performance of both MC-SVMs and other non-SVM learning algorithms. Ensemble classifiers do not generally improve performance of the best non-ensemble models. These results guided the construction of a software system GEMS (Gene Expression Model Selector) that automates high-quality model construction and enforces sound optimization and performance estimation procedures. This is the first such system to be informed by a rigorous comparative analysis of the available algorithms and datasets.
  
  
  BACKGROUND
  The software system GEMS is available for download from http://www.gems-system.org for non-commercial use.
  
  
  BACKGROUND
  alexander.statnikov@vanderbilt.edu.
authors.affiliation: Department of Biomedical Informatics, Vanderbilt University Nashville, TN, USA. alexander.statnikov@vanderbilt.edu
authors.forename: Alexander
authors.initials: A
authors.lastname: Statnikov
authors.forename: Constantin F
authors.initials: CF
authors.lastname: Aliferis
authors.forename: Ioannis
authors.initials: I
authors.lastname: Tsamardinos
authors.forename: Douglas
authors.initials: D
authors.lastname: Hardin
authors.forename: Shawn
authors.initials: S
authors.lastname: Levy
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000378:metabolism*; D016000:Cluster Analysis; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D020022:Genetic Predisposition to Disease / Q000235:genetics; D005820:Genetic Testing / Q000379:methods*; D006801:Humans; 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; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software; D014584:User-Computer Interface
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins