Combination Approaches Improve Predictive Performance of Diagnostic Rules for Mass-Spectrometry Proteomic Data
| Autor: | Werner Vach, Bart Mertens, Alexia Kakourou |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Proteomics
Disease status Computer science Calibration (statistics) Machine learning computer.software_genre Mass spectrometry Mass Spectrometry Genetics Humans clinical mass-spectrometry-based proteomics Molecular Biology Research Articles Models Statistical business.industry Computational Biology double cross validation Data set Computational Mathematics Computational Theory and Mathematics classification Modeling and Simulation Classification rule classifier combination Artificial intelligence Data mining Combination method business computer Algorithms |
| Zdroj: | Journal of Computational Biology, 21(12), 898-914 |
| Popis: | We consider a proteomic mass spectrometry case-control study for the construction of a diagnostic rule for patients' disease status allocation. We propose an approach for combining a collection of classifiers for the construction of a “combined” classification rule in order to enhance calibration and prediction ability. In a first stage this is achieved by building individual classifiers separately, each one using the entire proteomic data set. A double leave-one-out cross-validatory approach is used to estimate the class-predicted probabilities on which the combination method will be calibrated. The performance of the combination approach is examined both through a breast cancer proteomic data set and through simulation studies. Our experimental results indicate that in many circumstances gains in classification performance and predictive accuracy can be achieved. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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