Abstract
Three-way PCA has been applied to proteomic pattern images to identify the classes of samples present in the dataset. The developed method has been applied to two different datasets: a rat sera dataset, constituted by five samples of healthy Wistar rat sera and five samples of nicotine-treated Wistar rat sera; a human lymph-node dataset constituted by four healthy lymph-nodes and four lymph-nodes affected by a non-Hodgkin's lymphoma. The method proved to be successful in the identification of the classes of samples present in both of the groups of 2D-PAGE images, and it allowed us to identify the regions of the two-dimensional maps responsible for the differences occurring between the classes for both rat sera and human lymph-nodes datasets.
| Original language | English |
|---|---|
| Pages (from-to) | 351-360 |
| Number of pages | 10 |
| Journal | Journal of Proteome Research |
| Volume | 2 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - Jul 2003 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- 2D-maps
- Multivariate analysis
- Proteomics
- Three-way principal component analysis
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