 On the use of double cross-validation for the combination of proteomic mass spectral data for enhanced diagnosis and predictionB.J.A. Mertens, Y.E.M. van der Burgt, B. Velstra, W.E. Mesker, R.A.E.M. Tollenaar and A.M. Deelder Statistics & Probability Letters, 2011, vol. 81, issue 7, 759-766 Abstract: 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. Keywords: Clinical; mass; spectrometry; proteomics; Predictive; data; fusion; Double; cross-validation; Classification; Model; combination (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:81:y:2011:i:7:p:759-766 Ordering information: This journal article can be ordered from Access Statistics for this article Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul More articles in Statistics & Probability Letters from Elsevier |
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