0-7803-9387-2/05/$20.00 ©2005 IEEE Utilizing Artificial Neural Networks to Elucidate Serum Biomarker Patterns Which Discriminate Between Clinical Stages in Melanoma Lee Lancashire 1 Selma Ugurel 2 Colin Creaser 1 Dirk Schadendorf 2 Robert Rees 1 Graham Ball 1 1 Interdisciplinary Biomedical Research Centre, School of Science, Nottingham Trent University, Clifton Lane, Clifton, Nottingham NG11 8NS, United Kingdom. 2 Department of Dermatology, Skin Cancer unit, German Cancer Research Centre, Univeritatsklinkum Mannheim, Theodor Kutzer Ufer1, D-68135, Mannheim, Germany. Email: Lee.Lancashire@ntu.ac.uk 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. I. INTRODUCTION The ability to identify biomarker patterns indicative of onset or progression of diseases such as cancer would lead to the ability to identify the disease more rapidly, thus increasing the probability of successfully treating the patient whilst the disease is still in its early stages. Furthermore, this may lead to the development of novel assays capable of assisting in the diagnosis and management of patients on an individual basis, recently described as ‘personalised medicine’ [1]. Current advances in high throughput mass spectrometry (MS) technologies such as surface enhanced laser/desorption ionization (SELDI) time of flight (TOF) MS has permitted for the rapid identification of potential biomarkers associated with disease [2-7]. SELDI MS allows for the potential analysis of the proteome as a whole and generates patterns that these masses of proteins produce, thus facilitating the identification of proteins that are being expressed differently in different conditions, such as tumor stage [8]. We have previously shown that by using pattern recognition algorithms such as artificial neural networks (ANNs) as a data mining tool, it is possible to accurately predict disease outcome from complex SELDI MS datasets in both cancer patients [9-11], and in the classification of diseases from micro-organisms [12]. Here we apply a novel ANN methodology to re-analyze a dataset recently analyzed by our group [13]. This dataset consists of 205 melanoma serum samples, 106 of these being from patients with stage I disease, and the remaining 99 from stage IV patients, with the aim being to determine possible biomarkers present in the patients which could accurately predict disease stage, and would therefore have the potential to be used to detect disease onset at an earlier time point. A. ANNs ANNs are a form of machine learning and are capable of modelling for complex systems. They are based on the way a biological neuron operates in organising and processing information. This gives ANNs several advantages; for example, they are able to handle data which contains high levels of noise or redundancy, or that which contains non- linear elements. Perhaps the most popular form of ANN used is the multi-layer perceptron (MLP), which has been shown to provide a robust approach to outcome approximation, and has been used to great effect in the biological sciences [14-19]. The MLP in particular is being commonly used for the data mining of highly complex datasets [20-22], and models