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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 2897-2900 |
| Number of pages | 4 |
| Journal | Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings |
| Volume | 3 |
| State | Published - 2003 |
| Externally published | Yes |
| Event | A New Beginning for Human Health: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Cancun, Mexico Duration: Sep 17 2003 → Sep 21 2003 |
All Science Journal Classification (ASJC) codes
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics
Keywords
- Common characteristics and noise filtering
- Genetic algorithm
- Mass spectrum
- Normalization
- Proteomics
- SELDI