ArticlePloS one2013
Prioritizing disease candidate proteins in cardiomyopathy-specific protein-protein interaction networks based on "guilt by association" analysis.
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 7 papers.
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Who cites it
7 citing papers in PubMed.
- Analyzing host-viral interactome of SARS-CoV-2 for identifying vulnerable host proteins during COVID-19 pathogenesis.Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases · 2021Article
- Tau Mutations Serve as a Novel Risk Factor for Cancer.Cancer research · 2018Article
- Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences.Briefings in bioinformatics · 2018Review
- GeneHancer: genome-wide integration of enhancers and target genes in GeneCards.Database : the journal of biological databases and curation · 2017Article
- Predicting diabetes mellitus genes via protein-protein interaction and protein subcellular localization information.BMC genomics · 2016Article
- Decoding the complex genetic causes of heart diseases using systems biology.Biophysical reviews · 2015Article
- Degree-adjusted algorithm for prioritisation of candidate disease genes from gene expression and protein interactome.IET systems biology · 2014Article
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Authors and funding
12 authors.
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Abstract
The cardiomyopathies are a group of heart muscle diseases which can be inherited (familial). Identifying potential disease-related proteins is important to understand mechanisms of cardiomyopathies. Experimental identification of cardiomyophthies is costly and labour-intensive. In contrast, bioinformatics approach has a competitive advantage over experimental method. Based on "guilt by association" analysis, we prioritized candidate proteins involving in human cardiomyopathies. We first built weighted human cardiomyopathy-specific protein-protein interaction networks for three subtypes of cardiomyopathies using the known disease proteins from Online Mendelian Inheritance in Man as seeds. We then developed a method in prioritizing disease candidate proteins to rank candidate proteins in the network based on "guilt by association" analysis. It was found that most candidate proteins with high scores shared disease-related pathways with disease seed proteins. These top ranked candidate proteins were related with the corresponding disease subtypes, and were potential disease-related proteins. Cross-validation and comparison with other methods indicated that our approach could be used for the identification of potentially novel disease proteins, which may provide insights into cardiomyopathy-related mechanisms in a more comprehensive and integrated way.
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