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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Cava, C. Bertoli, G. Castiglioni, I. |
| Copyright Year | 2014 |
| Description | Author affiliation: Inst. of Mol. Bioimaging & Physiol., Milan, Italy (Cava, C.; Bertoli, G.; Castiglioni, I.) |
| Abstract | Microarray experiments have made possible to identify breast cancer marker gene signatures. However, gene expression-based signatures present limitations because they do not consider metabolic role of the genes and are affected by genetic heterogeneity across patient cohorts. Considering the activity of entire pathways rather than the expression levels of individual genes can be a way to exceed these limits. We evaluated and compared five methods of pathway-level aggregation of gene expression data. Our results confirmed the important role of pathway expression profile in breast cancer diagnostic classification (accuracy >90%). However, although assessed on a limited number of samples and datasets, this study shows that using dissimilarity representation among patients does not improve the classification of pathway-based expression profiles. |
| Sponsorship | IEEE Eng. Med. Biol. Soc. |
| Starting Page | 1151 |
| Ending Page | 1154 |
| File Size | 1044018 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424479290 |
| ISSN | 1557170X |
| DOI | 10.1109/EMBC.2014.6943799 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-08-26 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Tumors Breast cancer Gene expression Accuracy Bioinformatics Support vector machines |
| Content Type | Text |
| Resource Type | Article |
| Subject | Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition |
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