Utilizing Artificial Neural Networks to Elucidate Serum Biomarker Patterns Which Discriminate Between Clinical Stages in Melanoma

Abstract

The identification of proteomic patterns from biomarkers in diseases such as cancer could lead to the determination of novel prognostic and diagnostic markers fundamental to the treatment of patients. We apply a recently developed approach utilizing artificial neural networks as a data mining tool to identify and characterize the best subset of biomarkers associated with melanoma. These were capable of predicting whether a sample is from a patient diagnosed with stage I or stage IV melanoma to median accuracies of 98 % on an independent subset of data used for validation. Furthermore, individual response curves have been generated allowing the investigation of whether these markers are up or down regulated with regards to tumor progression.