Effect of data combination on predictive modeling: a study using gene expression data

Melanie Osl, Stephan Dreiseitl, Jihoon Kim, Kiltesh Patel, C. Baumgartner, Lucila Ohno-Machado

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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.

CONCLUSION: We identified volatility of feature selection as contributing factor to some of the problems faced by predictive modeling using microarray data.

Original languageEnglish
Pages (from-to)567-571
Number of pages5
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
Publication statusPublished - 2010


  • Gene Expression
  • Gene Expression Profiling
  • Models, Theoretical
  • Oligonucleotide Array Sequence Analysis


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