ArticlePloS one2013
Network topology reveals key cardiovascular disease genes.
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 24 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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed.
- Precision cardiovascular medicine: shifting the innovation paradigm.Frontiers in science · 2025Article
- Network analysis of atherosclerotic genes elucidates druggable targets.BMC medical genomics · 2022Article
- Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases.Frontiers in cardiovascular medicine · 2022Review
- Big Data in Cardiology: State-of-Art and Future Prospects.Frontiers in cardiovascular medicine · 2022Review
- GPCRs Are Optimal Regulators of Complex Biological Systems and Orchestrate the Interface between Health and Disease.International journal of molecular sciences · 2021Review
- Systems biology in cardiovascular disease: a multiomics approach.Nature reviews. Cardiology · 2021Review
- Exploring the Pharmacological Mechanisms ofBioMed research international · 2021Article
- Network-Based Genetic Profiling Reveals Cellular Pathway Differences Between Follicular Thyroid Carcinoma and Follicular Thyroid Adenoma.International journal of environmental research and public health · 2020Article
- Integrative gene ontology and network analysis of coronary artery disease associated genes suggests potential role of ErbB pathway gene EGFR.Molecular medicine reports · 2018Article
- Identification of target gene of venous thromboembolism in patients with lymphoma via microarray analysis.Oncology letters · 2017Article
- Genome-Wide Linkage Analysis of Large Multiple Multigenerational Families Identifies Novel Genetic Loci for Coronary Artery Disease.Scientific reports · 2017Article
- The CRISPR/Cas9 System and the Possibility of Genomic Edition for Cardiology.Arquivos brasileiros de cardiologia · 2017Article
- Bioinformatic Analysis of Coronary Disease Associated SNPs and Genes to Identify Proteins Potentially Involved in the Pathogenesis of Atherosclerosis.Journal of proteomics and genomics research · 2017Article
- A systematic study on drug-response associated genes using baseline gene expressions of the Cancer Cell Line Encyclopedia.Scientific reports · 2016Article
- BLAT2DOLite: An Online System for Identifying Significant Relationships between Genetic Sequences and Diseases.PloS one · 2016Article
- CardioGenBase: A Literature Based Multi-Omics Database for Major Cardiovascular Diseases.PloS one · 2015Article
- Network-based association of hypoxia-responsive genes with cardiovascular diseases.New journal of physics · 2014Article
- Predicting disease associations via biological network analysis.BMC bioinformatics · 2014Article
- Integration of molecular network data reconstructs Gene Ontology.Bioinformatics (Oxford, England) · 2014Article
- Predicting disease-related proteins based on clique backbone in protein-protein interaction network.International journal of biological sciences · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The structure of protein-protein interaction (PPI) networks has already been successfully used as a source of new biological information. Even though cardiovascular diseases (CVDs) are a major global cause of death, many CVD genes still await discovery. We explore ways to utilize the structure of the human PPI network to find important genes for CVDs that should be targeted by drugs. The hope is to use the properties of such important genes to predict new ones, which would in turn improve a choice of therapy. We propose a methodology that examines the PPI network wiring around genes involved in CVDs. We use the methodology to identify a subset of CVD-related genes that are statistically significantly enriched in drug targets and "driver genes." We seek such genes, since driver genes have been proposed to drive onset and progression of a disease. Our identified subset of CVD genes has a large overlap with the Core Diseasome, which has been postulated to be the key to disease formation and hence should be the primary object of therapeutic intervention. This indicates that our methodology identifies "key" genes responsible for CVDs. Thus, we use it to predict new CVD genes and we validate over 70% of our predictions in the literature. Finally, we show that our predicted genes are functionally similar to currently known CVD drug targets, which confirms a potential utility of our methodology towards improving therapy for CVDs.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.