On the use of double cross-validation for the combination of proteomic mass spectral data for enhanced diagnosis and prediction
| Autor: | Y.E.M. van der Burgt, Wilma E. Mesker, Bart Mertens, A.M. Deelder, Berit Velstra, Rob A. E. M. Tollenaar |
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| Rok vydání: | 2011 |
| Předmět: |
Statistics and Probability
business.industry Posterior probability Pattern recognition Mass spectrometry Cross-validation Discriminant Statistics Prior probability Mass spectrum Calibration Clinical mass spectrometry proteomics Predictive data fusion Double cross-validation Classification Model combination spectrometry Artificial intelligence Statistics Probability and Uncertainty business Spectral data Mathematics |
| Zdroj: | Statistics and Probability Letters, 81(7), 759-766 |
| ISSN: | 0167-7152 |
| DOI: | 10.1016/j.spl.2011.02.037 |
| Popis: | We consider a proteomic mass spectrometry case-control study for the calibration of a diagnostic rule for the detection of early-stage breast cancer. For each patient, a pair of two distinct mass spectra is recorded, each of which is derived from a different prior fractionation procedure on the available patient serum. We propose a procedure for combining the distinct spectral expressions from patients for the calibration of a diagnostic discriminant rule. This is achieved by first calibrating two distinct prediction rules separately, each on only one of the two available spectral data sources. A double cross-validatory approach is used to summarize the available spectral data using the two classifiers to posterior class probabilities, on which a combined predictor can be calibrated. (C) 2011 Elsevier B.V. All rights reserved. |
| Databáze: | OpenAIRE |
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