Decision tree classification of proteins identified by mass spectrometry of blood serum samples from people with and without lung cancer |
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Authors: | Markey Mia K Tourassi Georgia D Floyd Carey E |
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Institution: | Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712-1084, USA. mia.markey@mail.utexas.edu |
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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. |
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