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

Markey Mia K. | Zendy; Tourassi Georgia D. | Zendy; Floyd Carey E. | Zendy

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

Author(s) -

Markey Mia K.,

Tourassi Georgia D.,

Floyd Carey E.

Publication year - 2003

Publication title -

proteomics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.26

H-Index - 167

eISSN - 1615-9861

pISSN - 1615-9853

DOI - 10.1002/pmic.200300521

Subject(s) - cart , decision tree , mass spectrometry , receiver operating characteristic , lung cancer , regression analysis , cross validation , tree (set theory) , regression , biology , computational biology , chromatography , chemistry , pathology , statistics , medicine , artificial intelligence , mathematics , computer science , geography , mathematical analysis , archaeology

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.

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