ArticlePloS one2013
Quantitative network measures as biomarkers for classifying prostate cancer disease states: a systems approach to diagnostic biomarkers.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Review
- Genetic tests in major psychiatric disorders-integrating molecular medicine with clinical psychiatry-why is it so difficult?Translational psychiatry · 2017Review
- A Glimpse to Background and Characteristics of Major Molecular Biological Networks.BioMed research international · 2015Review
- Dry computational approaches for wet medical problems.Journal of translational medicine · 2014Article
- Why network approach can promote a new way of thinking in biology.Frontiers in genetics · 2014Article
- Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks.Frontiers in cell and developmental biology · 2014Review
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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