Conference proceeding
Common characteristics and noise filtering and its application in a proteomic pattern recognition system for cancer detection
Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439), v 3, pp 2897-2900 Vol.3
2003
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
High-throughput mass spectrometry technologies, such as surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-ToF-MS), generate large sets of complex data. The high dimensionality of these datasets poses analytical and computational challenges to the task of spectrum classification. In this paper, we describe a common characteristics and noise filter, which hones in on spectrum subsets with high discriminatory power. The filter is incorporated in a proteomic pattern recognition system. Our method is demonstrated on a set of 322 SELDI-ToF mass spectra of serum samples from prostate cancer patients and a control group. We show that our system can extract the discriminatory subsets from these spectra, and improve classification accuracy and computational speed compared to existing techniques.
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Details
- Title
- Common characteristics and noise filtering and its application in a proteomic pattern recognition system for cancer detection
- Creators
- Lit-Hsin Loo - Drexel UniversityJ Quinn - Drexel UniversityH Cordingley - (GlaxoSmithKline)S RobertsL Hrebien - Drexel UniversityM Kam - Drexel University
- Publication Details
- Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439), v 3, pp 2897-2900 Vol.3
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering; General Internal Medicine
- Web of Science ID
- WOS:000189395300752
- Other Identifier
- 991019167642104721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Cardiac & Cardiovascular Systems
- Computer Science, Interdisciplinary Applications
- Engineering, Biomedical
- Medicine, Research & Experimental
- Neurosciences
- Radiology, Nuclear Medicine & Medical Imaging