Abstract
Identifying diagnostic biomarkers based on genomic features for an accurate disease classification is a problem of great importance for both, basic medical research and clinical practice. In this paper, we introduce quantitative network measures as structural biomarkers and investigate their ability for classifying disease states inferred from gene expression data from prostate cancer. We demonstrate the utility of our approach by using eigenvalue and entropy-based graph invariants and compare the results with a conventional biomarker analysis of the underlying gene expression data.
| Original language | English |
|---|---|
| Article number | 77602 |
| Number of pages | 8 |
| Journal | PLoS ONE |
| Volume | 8 |
| Issue number | 11 |
| DOIs | |
| Publication status | Published - 13 Nov 2013 |
| Publication type | A1 Journal article-refereed |
Funding
Matthias Dehmer thanks the Austrian Science Funds for supporting this work (project P22029-N13). The authors also thank the 'Zentraler Informatikdienst' of the Technical University of Vienna for providing computing resources to perform large scale computations on the Phoenix Cluster. Also, Matthias Dehmer and Laurin Mueller thank the Standortagentur Tirol for supporting this work. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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
- GENE-EXPRESSION
- TOPOLOGICAL INDEXES
- INFORMATION CONTENT
- ENTROPY MEASURES
- GRAPHS
- COMPLEXITY
- DISCRIMINATION
- CELLS
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