title: Decision tree classification of proteins identified by mass spectrometry of blood serum samples from people with and without lung cancer.
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.
authors.affiliation: Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712-1084, USA. mia.markey@mail.utexas.edu
authors.forename: Mia K
authors.initials: MK
authors.lastname: Markey
authors.forename: Georgia D
authors.initials: GD
authors.lastname: Tourassi
authors.forename: Carey E
authors.initials: CE
authors.lastname: Floyd
mesh_terms: D001798:Blood Proteins / Q000737:chemistry / Q000145:classification* / Q000378:metabolism; D019295:Computational Biology; D030562:Databases, Protein; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D008175:Lung Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000378:metabolism; D013058:Mass Spectrometry / Q000379:methods*; D012189:Retrospective Studies
chemical_list: D001798:Blood Proteins