Limits...
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.

Show MeSH

Related in: MedlinePlus

The number of proteins related with DCM.50 potential disease proteins identified either by our developed method (the top left circle), or by Chen’s protein ranking method (the top right circle), and the number of proteins which have been confirmed to be related with DCM in literature were plotted.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3733802&req=5

pone-0071191-g004: The number of proteins related with DCM.50 potential disease proteins identified either by our developed method (the top left circle), or by Chen’s protein ranking method (the top right circle), and the number of proteins which have been confirmed to be related with DCM in literature were plotted.

Mentions: To further test the performance of our method, top 50 candidate proteins from our method were compared with those from Chen’s protein ranking method by literature review. The result for DCM was shown in Figure 4. We found 36 proteins in both protein sets, in which 8 were confirmed to be DCM-related (Table 2). Among the remaining proteins, 8 in our potential protein list (Table 2) and 5 in Chen’s protein ranking list [69]–[73] were found to be related to the processes associated with DCM.


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)

The number of proteins related with DCM.50 potential disease proteins identified either by our developed method (the top left circle), or by Chen’s protein ranking method (the top right circle), and the number of proteins which have been confirmed to be related with DCM in literature were plotted.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0071191-g004: The number of proteins related with DCM.50 potential disease proteins identified either by our developed method (the top left circle), or by Chen’s protein ranking method (the top right circle), and the number of proteins which have been confirmed to be related with DCM in literature were plotted.
Mentions: To further test the performance of our method, top 50 candidate proteins from our method were compared with those from Chen’s protein ranking method by literature review. The result for DCM was shown in Figure 4. We found 36 proteins in both protein sets, in which 8 were confirmed to be DCM-related (Table 2). Among the remaining proteins, 8 in our potential protein list (Table 2) and 5 in Chen’s protein ranking list [69]–[73] were found to be related to the processes associated with DCM.

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