title: Effect of data combination on predictive modeling: a study using gene expression data.
abstract: BACKGROUND
  The quality of predictive modeling in biomedicine depends on the amount of data available for model building.
  
  
  OBJECTIVE
  To study the effect of combining microarray data sets on feature selection and predictive modeling performance.
  
  
  METHODS
  Empirical evaluation of stability of feature selection and discriminatory power of classifiers using three previously published gene expression data sets, analyzed both individually and in combination.
  
  
  RESULTS
  Feature selection was not robust for the individual as well as for the combined data sets. The classification performance of models built on individual and combined data sets was heavily dependent on the data set from which the features were extracted.
  
  
  CONCLUSIONS
  We identified volatility of feature selection as contributing factor to some of the problems faced by predictive modeling using microarray data.
authors.affiliation: Dept. of Biomedical Sciences and Engineering, University of Health Sciences, Medical Informatics and Technology, Hall, Austria.
authors.forename: Melanie
authors.initials: M
authors.lastname: Osl
authors.forename: Stephan
authors.initials: S
authors.lastname: Dreiseitl
authors.forename: Jihoon
authors.initials: J
authors.lastname: Kim
authors.forename: Kiltesh
authors.initials: K
authors.lastname: Patel
authors.forename: Christian
authors.initials: C
authors.lastname: Baumgartner
authors.forename: Lucila
authors.initials: L
authors.lastname: Ohno-Machado
mesh_terms: D015870:Gene Expression; D020869:Gene Expression Profiling; D008962:Models, Theoretical; D020411:Oligonucleotide Array Sequence Analysis