TITLE:
Comparing biomarkers and proteomic fingerprints for classification studies
AUTHORS:
Brian T. Luke, Jack R. Collins, Jens K. Habermann, DaRue A. Prieto, Timothy D. Veenstra, Thomas Ried
KEYWORDS:
Biomarker; Classifier; Proteomic Fingerprint
JOURNAL NAME:
Journal of Biomedical Science and Engineering,
Vol.6 No.4,
April
29,
2013
ABSTRACT: Early disease detection is extremely important in the treatment and
prognosis of many diseases, especially cancer. Often, proteomic fingerprints
and a pattern recognition algorithm are used to classify the pathological
condition of a given individual. It has been argued that accurate
classification of the existing data implies an underlying biological
significance. Two fingerprint-based classifiers, decision tree and medoid
classification algorithm, and a biomarker-based classifier were examined using
a published dataset of mass spectral peaks from 81 healthy individuals and 78 individuals with benign prostate hyperplasia (BPH). For all three methods, classifiers were constructed using the original
data and the data after permuting the labels of the samples (BPH and healthy).
The fingerprint-based classifiers produced accurate results for the original
data, though the peaks used in a given classifier depended upon which samples
were placed in the training set. Accurate results were also obtained for the
dataset with permuted labels. In contrast, only three unique peaks were
identified as putative biomarkers, producing a small number of reasonably
accurate biomarker-based classifiers. The dataset with permuted labels was
poorly classified. Since fingerprint-based classifiers accurately classified
the dataset with permuted labels, the argument for biological significance from
a fingerprint-based classifier must be questioned.