Quantitative network measures as biomarkers for classifying prostate cancer disease states: a systems approach to diagnostic biomarkers.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 24236006.
- Also identified by DOI 10.1371/journal.pone.0077602 and PMC identifier 3827206.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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.
Medical subject headings
- Biomarkers, Tumor
- Gene Expression Profiling
- Models, Biological
- Prostatic Neoplasms