Abstract
In the presented work, we propose two approaches to study the similarity between proteomic maps. First, we introduced similarity index as a robust parameter to express the degree of similarity among proteomic maps. Second, we treated the proteomic maps with self-organizing map (SOM) technique taking the abundances of spots as input variables. The study was performed on a set of reported proteomic maps, which were experimentally derived from mouse liver after the animals were treated with peroxisome proliferators. (Entire study included five peroxisome proliferators, control, and a non-peroxisome proliferating compound.) In original paper, the authors analyzed the data with the principal component method. Later other authors reported the study on the same data set using different numerical representations of proteomic maps. The presented results are in agreement with reported conclusions.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 33-38 |
| Number of pages | 6 |
| Journal | Chemometrics and Intelligent Laboratory Systems |
| Volume | 70 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 28 2004 |
| Externally published | Yes |
Bibliographical note
Funding Information:MV gratefully thanks the Ministry of Education, Science and Sport of the Republic of Slovenia that supported this work under contracts P1-034507 and SLO-US 021. Research reported in this paper was supported in part by Grant F49620-02-1-0138 from the United States Air Force. This is contribution number 349 from the Center for Water and the Environment of the Natural Resources Research Institute.
Keywords
- Peroxisome proliferators
- Proteomic maps
- Self-organizing map
- Similarity index