Abstract
We use clustering-based algorithms to classify polypeptide spectra of treated rat liver samples obtained through surface enhanced laser desorption/ ionization mass spectrometry (SELDI-MS). Variance analysis is used to extract useful features from the high dimensional datasets. The features are then clustered using a hierarchical clustering algorithm based on scaled Euclidean distances. The clusters created are found to be correlated to the toxicity of the rat liver samples.
Original language | English (US) |
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Pages (from-to) | 139-140 |
Number of pages | 2 |
Journal | Proceedings of the IEEE Annual Northeast Bioengineering Conference, NEBEC |
State | Published - 2002 |
Externally published | Yes |
Event | IEEE 28th Annual Northeast Bioengineering Conference - Philadelphia, PA, United States Duration: Apr 20 2002 → Apr 21 2002 |
All Science Journal Classification (ASJC) codes
- General Chemical Engineering
- Bioengineering
Keywords
- Hepatotoxicity
- Hierarchical clustering
- Matrix assisted laser desorption/ionization
- Polypeptide spectra
- Predictive toxicology
- SELDI