Decision tree classification of proteins identified by mass spectrometry of blood serum samples from people with and without lung cancer

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 cros...

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Bibliographic Details
Published in:Proteomics (Weinheim) 2003-09, Vol.3 (9), p.1678-1679
Main Authors: Markey, Mia K., Tourassi, Georgia D., Floyd Jr, Carey E.
Format: Article
Language:English
Subjects:
Blood Proteins - chemistry
Blood Proteins - classification
Blood Proteins - metabolism
Classification
classification and regression tree
Computational Biology
Computer-aided diagnosis
Databases, Protein
Decision tree
Decision tree, classification and regression tree
Diagnosis, Computer-Assisted
Humans
Lung Neoplasms - classification
Lung Neoplasms - diagnosis
Lung Neoplasms - metabolism
Mass spectrometry
Mass Spectrometry - methods
Retrospective Studies
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Summary: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.
ISSN:1615-9853
1615-9861
DOI:10.1002/pmic.200300521