Here the inventors describe a tumor classifier based on protein expression. Also disclosed is the use of proteomics to construct a highly accurate artificial neural network (ANN)-based classifier for the detection of an individual tumor type, as well as distinguishing between six common tumor types in an unknown primary diagnosis setting. Discriminating sets of proteins are also identified and are used as biomarkers for six carcinomas. A leave-one-out cross validation (LOOCV) method was used to test the ability of the constructed network to predict the single held out sample from each iteration with a maximum predictive accuracy of 87% and an average predictive accuracy of 82% over the range of proteins chosen for its construction.
Yeatman, Timothy J.; Zhou, Jeff Xiwu; Bloom, Gregory C.; and Eschrich, Steven A., "Artificial neural network proteomic tumor classification" (2014). USF Patents. 87.
H. Lee Moffitt Cancer Center and Research Institute, Inc. University of South Florida