Human Disease-Protein Network

Author(s): Yangmei Cheng, Hao Zhang, Hui Zheng*, Jun Zhang*, Yang Hu, Liang Cheng.

Journal Name: Current Proteomics

Volume 15 , Issue 2 , 2018

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Graphical Abstract:


Background: Using system biology data to investigate diseases is a tendency. In consideration that protein is the functional unit of human body in molecule level, it is a straight way to view the relationships among diseases from the perspective of human proteins. However, lack of disease annotations of human proteins limit this purpose.

Objective: Our objective is to present a framework for extracting associations between diseases and proteins first, and then constructed human disease network (HDN) based on disease-related proteins.

Method: The protein-disease associations were extracted from UniProt, which involves disease descriptions of human proteins. Each description contains an Online Mendelian Inheritance in Man (OMIM) id or a text. OMIM ids of the descriptions were mapped to Comparative Toxicogenomics Database (CTD)'s ‘merged disease vocabulary' (MEDIC), and disease terms of the texts were annotated to MEDIC using MGREP. Relativity scores of disease pairs were calculated based on Jaccard Index for establishing the HDN, where a node represents a disease and an edge of pair-wise diseases indicates their relativity score more than zero.

Results: 4,466 associations between 2,933 diseases and 2,625 proteins were obtained. The degree distribution of the diseases in the HDN revealed a power-law distribution with R2 = 0.9762, which shows that the network displayed scale-free characteristics like many other biological networks.

Conclusion: Here, we constructed a HDN by our protein-disease annotations. As our expectation, hub nodes of the network are always disease classes or complex diseases. In comparison, the most similar diseases are always specific diseases.

Keywords: Disease annotations, hub nodes, human disease network, Jaccard Index, protein, relativity score, scale-free.

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Article Details

Year: 2018
Page: [159 - 164]
Pages: 6
DOI: 10.2174/1570164614666171031154904
Price: $58

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