Title :
Predictive Modeling in Proteomics-based Disease Detection
Author_Institution :
James Cook Univ., Townsville
Abstract :
Recent advent of mass-spectrometry data generated by proteomic technology provides a new type of biological information which is very promising in the search for diagnostic and therapeutic approaches that enables the early detection of fatal diseases and the development of personalized medicine. Successful analysis of such high-throughput proteomic data relies much on signal-processing and pattern-recognition techniques. This paper addresses the application of prediction models for cancer detection using mass spectral data.
Keywords :
cancer; mass spectra; medical signal detection; medical signal processing; patient diagnosis; pattern recognition; proteins; biological information; cancer detection; diagnostic approaches; fatal diseases; mass-spectrometry data; pattern recognition; personalized medicine; predictive modeling; proteomic data; proteomics-based disease detection; signal processing; therapeutic approaches; Bioinformatics; Biomarkers; Cancer detection; Diseases; Distortion measurement; Genomics; Humans; Predictive models; Proteins; Proteomics; Algorithms; Computer Simulation; Diagnosis, Computer-Assisted; Female; Gene Expression Profiling; Humans; Models, Biological; Neoplasm Proteins; Ovarian Neoplasms; Proteome; Proteomics; Reproducibility of Results; Sensitivity and Specificity; Tumor Markers, Biological;
Conference_Titel :
Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE
Conference_Location :
Lyon
Print_ISBN :
978-1-4244-0787-3
DOI :
10.1109/IEMBS.2007.4353037
