Journal
PROTEOMICS
Volume 6, Issue 22, Pages 5903-5914Publisher
WILEY
DOI: 10.1002/pmic.200600375
Keywords
biomarkers; data management; heart failure; mass spectrometry; normalisation
Funding
- MRC [G0100873] Funding Source: UKRI
- Medical Research Council [G0100873] Funding Source: researchfish
- Medical Research Council [G0100873] Funding Source: Medline
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We have developed a technique for analysing blood plasma using MALDI-MS with subsequent data analysis to identify significant and specific differences between heart failure (HF) patients and healthy individuals. A training dataset comprising 100 HF patients and 100 healthy individuals was used to search for biomarkers (m/z range 1000-10000). EWP cartridges when used in tandem with microcon centrifugal filters were found to give the best results. A data management chain including event binning, background subtraction and feature extraction was developed to reduce the data, and statistical analysis was used to map feature intensities on to a common scale. Various mathematical approaches including a simple cumulative score, support vector machines (SVM) and genetic algorithms (GAs) were then used to combine the results from individual features and provide a robust classification algorithm. The SVM gave the most promising results (accuracy 95%, receiver operating characteristic (ROC) score of 0.997 using 18 selected features). Finally, a test dataset comprising a further 32 HF patients and 20 controls was used to verify that the 18 putative biornarkers and classification algorithms gave reliable predictions (accuracy 88.5%, ROC score 0.998).
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