AMIA Annu Symp Proc - Effect of data combination on predictive modeling: a study using gene expression data.

Tópicos

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Resumo

CKGROUND: 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.

Resumo Limpo

ckground qualiti predict model biomedicin depend amount data avail model buildingobject studi effect combin microarray data set featur select predict model performancemethod empir evalu stabil featur select discriminatori power classifi use three previous publish gene express data set analyz individu combinationresult featur select robust individu well combin data set classif perform model built individu combin data set heavili depend data set featur extractedconclus identifi volatil featur select contribut factor problem face predict model use microarray data

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