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Prioritizing disease candidate proteins in cardiomyopathy-specific protein-protein interaction networks based on "guilt by association" analysis.

Li W, Chen L, He W, Li W, Qu X, Liang B, Gao Q, Feng C, Jia X, Lv Y, Zhang S, Li X - PLoS ONE (2013)

Bottom Line: We then developed a method in prioritizing disease candidate proteins to rank candidate proteins in the network based on "guilt by association" analysis.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.

View Article: PubMed Central - PubMed

Affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.

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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Related in: MedlinePlus

DCM pathway and its relevant pathways.DCM pathway is colored in yellow. Purple nodes are DCM-related pathways, and green nodes are other pathways. Black edges connect pathways which are directly connected to the DCM pathway.
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pone-0071191-g003: DCM pathway and its relevant pathways.DCM pathway is colored in yellow. Purple nodes are DCM-related pathways, and green nodes are other pathways. Black edges connect pathways which are directly connected to the DCM pathway.

Mentions: KEGG pathway enrichment analysis (p<0.05) was performed for the top 50 candidate proteins to illustrate the relationships between disease pathways of three subtypes of cardiomyopathies and other pathways (Figure 3, Figure S3, and S4). It was shown that DCM disease pathway was related to both HCM and ARVC pathways. DCM-related pathways that DCM seed genes enriched in were in the inner space (Figure 3).


Prioritizing disease candidate proteins in cardiomyopathy-specific protein-protein interaction networks based on "guilt by association" analysis.

Li W, Chen L, He W, Li W, Qu X, Liang B, Gao Q, Feng C, Jia X, Lv Y, Zhang S, Li X - PLoS ONE (2013)

DCM pathway and its relevant pathways.DCM pathway is colored in yellow. Purple nodes are DCM-related pathways, and green nodes are other pathways. Black edges connect pathways which are directly connected to the DCM pathway.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3733802&req=5

pone-0071191-g003: DCM pathway and its relevant pathways.DCM pathway is colored in yellow. Purple nodes are DCM-related pathways, and green nodes are other pathways. Black edges connect pathways which are directly connected to the DCM pathway.
Mentions: KEGG pathway enrichment analysis (p<0.05) was performed for the top 50 candidate proteins to illustrate the relationships between disease pathways of three subtypes of cardiomyopathies and other pathways (Figure 3, Figure S3, and S4). It was shown that DCM disease pathway was related to both HCM and ARVC pathways. DCM-related pathways that DCM seed genes enriched in were in the inner space (Figure 3).

Bottom Line: We then developed a method in prioritizing disease candidate proteins to rank candidate proteins in the network based on "guilt by association" analysis.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.

View Article: PubMed Central - PubMed

Affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.

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.

Show MeSH
Related in: MedlinePlus