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

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.

[1]  Graham Ball,et al.  A prototype methodology combining surface‐enhanced laser desorption/ionization protein chip technology and artificial neural network algorithms to predict the chemoresponsiveness of breast cancer cell lines exposed to Paclitaxel and Doxorubicin under in vitro conditions , 2003, Proteomics.

[2]  E. Petricoin,et al.  Clinical proteomics: translating benchside promise into bedside reality , 2002, Nature Reviews Drug Discovery.

[3]  E. Petricoin,et al.  Clinical proteomics: personalized molecular medicine. , 2001, JAMA.

[4]  M. Ringnér,et al.  Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks , 2001, Nature Medicine.

[5]  Yianni Attikiouzel,et al.  Artificial Neural Networks and Breast Cancer Prognosis , 1994, Aust. Comput. J..

[6]  E. Petricoin,et al.  Use of proteomic patterns in serum to identify ovarian cancer , 2002, The Lancet.

[7]  C. Skibola,et al.  Identification of biomarkers of arsenic exposure and metabolism in urine using SELDI technology , 2005, Journal of biochemical and molecular toxicology.

[8]  J. Wei,et al.  Understanding artificial neural networks and exploring their potential applications for the practicing urologist. , 1998, Urology.

[9]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[10]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[11]  C. Paweletz,et al.  Proteomic analysis of human bladder tissue using SELDI approach following microdissection techniques. , 2005, Methods in molecular biology.

[12]  D. Palmer-Brown,et al.  Towards unravelling the complex interactions between microclimate, ozone dose, and ozone injury in clover , 1995 .

[13]  K. Soman,et al.  Differential protein expression profiles of gastric epithelial cells following Helicobacter pylori infection using ProteinChips. , 2005, Journal of proteome research.

[14]  Graham R. Ball,et al.  Classification of bacterial species from proteomic data using combinatorial approaches incorporating artificial neural networks, cluster analysis and principal components analysis , 2005, Bioinform..

[15]  G. Ball,et al.  Identification of non-linear influences on the seasonal ozone dose-response of sensitive and resistant clover clones using artificial neural networks , 2000 .

[16]  Ian O. Ellis,et al.  Current Developments in the Analysis of Proteomic Data: Artificial Neural Network Data Mining Techniques for the Identification of Proteomic Biomarkers Related to Breast Cancer , 2005 .

[17]  Carsten Peterson,et al.  Analyzing tumor gene expression profiles , 2003, Artif. Intell. Medicine.

[18]  Amitabh Chak,et al.  Prediction of outcome in acute lower-gastrointestinal haemorrhage based on an artificial neural network: internal and external validation of a predictive model , 2003, The Lancet.

[19]  G. Li,et al.  An integrated approach utilizing artificial neural networks and SELDI mass spectrometry for the classification of human tumours and rapid identification of potential biomarkers , 2002, Bioinform..

[20]  Graham Ball,et al.  Preliminary artificial neural network analysis of Seldi mass spectrometry data for the classification of melanoma tissue , 2003 .

[21]  A W Partin,et al.  Artificial neural network model for the assessment of lymph node spread in patients with clinically localized prostate cancer. , 2001, Urology.

[22]  Mesut Remzi,et al.  Artificial neural networks for decision-making in urologic oncology. , 2003, Reviews in urology.

[23]  I. Dryden,et al.  Serum proteomic fingerprinting discriminates between clinical stages and predicts disease progression in melanoma patients. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[24]  Emanuel F Petricoin,et al.  Mass spectrometry-based diagnostics: the upcoming revolution in disease detection. , 2003, Clinical chemistry.