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

• Published in: Proteomics (Weinheim) Vol. 3; no. 9; pp. 1678 - 1679
• Main Authors: Markey, Mia K., Tourassi, Georgia D., Floyd Jr, Carey E.
• Format: Journal Article
• Language: English
• Published: Weinheim WILEY-VCH Verlag 01.09.2003; WILEY‐VCH Verlag
• 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
• ISSN: 1615-9853; 1615-9861
• DOI: 10.1002/pmic.200300521

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.