Examining the significance of fingerprint-based classifiers

Background: Experimental examinations of biofluids to measure concentrations of proteins or their fragments or metabolites are being explored as a means of early disease detection, distinguishing diseases with similar symptoms, and drug treatment efficacy. Many studies have produced classifiers with a high sensitivity and specificity, and it has been argued that accurate results necessarily imply some underlying biology-based features in the classifier. The simplest test of this conjecture is to examine datasets designed to contain no information with classifiers used in many published studies. Results: The classification accuracy of two fingerprint-based classifiers, a decision tree (DT) algorithm and a medoid classification algorithm (MCA), are examined. These methods are used to examine 30 artificial datasets that contain random concentration levels for 300 biomolecules. Each dataset contains between 30 and 300 Cases and Controls, and since the 300 observed concentrations are rando...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Brian T. Luke, Jack R. Collins
• Keywords: Average Sensitivity, BMCBI 2008, Drug Treatment Efficacy, Fingerprint-based Classifiers