ArticleCancer informatics2010
Protein-protein interaction reveals synergistic discrimination of cancer phenotype.
Article in Cancer informatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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
4 citing papers in PubMed.
- Multi-Omic Data Interpretation to Repurpose Subtype Specific Drug Candidates for Breast Cancer.Frontiers in genetics · 2019Article
- Personalized Medicine and Molecular Interaction Networks in Amyotrophic Lateral Sclerosis (ALS): Current Knowledge.Journal of personalized medicine · 2018Review
- Article
- Challenges in Biomarker Discovery: Combining Expert Insights with Statistical Analysis of Complex Omics Data.Expert opinion on medical diagnostics · 2013Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer is a disease associated with the deregulation of multiple gene networks. Microarray data has permitted researchers to identify gene panel markers for diagnosis or prognosis of cancer but these are not sufficient to make specific mechanistic assertions about phenotype switches. We propose a strategy to identify putative mechanisms of cancer phenotypes by protein-protein interactions (PPI). We first extracted the logic status of a PPI via the relative expression of the corresponding gene pair. The joint association of a gene pair on a cancer phenotype was calculated by entropy minimization and assessed using a support vector machine. A typical predictor is "If Src high-expression, and Cav-1 low-expression, then cancer." We achieved 90% accuracy on test data with a majority of predictions associated with the MAPK pathway, focal adhesion, apoptosis and cell cycle. Our results can aid in the development of phenotype discrimination biomarkers and identification of putative therapeutic interference targets for drug development.
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Registered trials
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