Predictive Modeling in Proteomics-based Disease Detection

Author

Pham, T.D.

Author_Institution

James Cook Univ., Townsville

fYear

2007

fDate

22-26 Aug. 2007

Firstpage

3308

Lastpage

3311

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE

Conference_Location

Lyon

ISSN

1557-170X

Print_ISBN

978-1-4244-0787-3

Type

conf

DOI

10.1109/IEMBS.2007.4353037

Filename

4353037