Decision tree classification of proteins identified by mass spectrometry of blood serum samples from people with and without lung cancer
- 9 September 2003
- journal article
- research article
- Published by Wiley in Proteomics
- Vol. 3 (9) , 1678-1679
- https://doi.org/10.1002/pmic.200300521
Abstract
A classification and regression tree (CART) model was trained to classify 41 clinical specimens as disease/nondisease based on 26 variables computed from the mass‐to‐charge ratio (m/z) and peak heights of proteins identified by mass spectroscopy. The CART model built on all of the specimens (no cross‐validation) had an error rate of 4/41 = 10%. The CART model suggests that mass spectra peaks in the 8000–10 000, 20 000–30 000, 45 000–60 000, and >125 000 m/z ranges may be valuable in distinguishing between the disease/nondisease specimens. The area under the receiver operating characteristics curve was 0.80 ± 0.07 for leave‐one‐out cross‐validation.Keywords
This publication has 1 reference indexed in Scilit:
- Basic principles of ROC analysisSeminars in Nuclear Medicine, 1978