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Title: Graphical features of functional genes in human protein interaction network
Authors: Wang, P
Chen, Y
Lü, J
Wang, Q
Yu, X
Keywords: Cluster
Disease classification
Functional gene
Human protein interaction network
Receiver operating characteristic (ROC) curve
Topological structure
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on biomedical circuits and systems, 2016, v. 10, no. 3, 7364299, p. 707-720 How to cite?
Journal: IEEE transactions on biomedical circuits and systems 
Abstract: With the completion of the human genome project, it is feasible to investigate large-scale human protein interaction network (HPIN) with complex networks theory. Proteins are encoded by genes. Essential, viable, disease, conserved, housekeeping (HK) and tissue-enriched (TE) genes are functional genes, which are organized and functioned via interaction networks. Based on up-to-date data from various databases or literature, two large-scale HPINs and six subnetworks are constructed. We illustrate that the HPINs and most of the subnetworks are sparse, small-world, scale-free, disassortative and with hierarchical modularity. Among the six subnetworks, essential, disease and HK subnetworks are more densely connected than the others. Statistical analysis on the topological structures of the HPIN reveals that the lethal, the conserved, the HK and the TE genes are with hallmark graphical features. Receiver operating characteristic (ROC) curves indicate that the essential genes can be distinguished from the viable ones with accuracy as high as almost 70%. Closeness, semi-local and eigenvector centralities can distinguish the HK genes from the TE ones with accuracy around 82%. Furthermore, the Venn diagram, cluster dendgrams and classifications of disease genes reveal that some classes of disease genes are with hallmark graphical features, especially for cancer genes, HK disease genes and TE disease genes. The findings facilitate the identification of some functional genes via topological structures. The investigations shed some light on the characteristics of the compete interactome, which have potential implications in networked medicine and biological network control.
ISSN: 1932-4545 (print)
1940-9990 (online)
DOI: 10.1109/TBCAS.2015.2487299
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