%0 Journal Article %1 Karabekmez2015Novel %A Karabekmez, Muhammed E. %A Kirdar, Betul %D 2015 %I The Royal Society of Chemistry %J Mol. BioSyst. %K centrality networks %R 10.1039/c5mb00732a %T A novel topological centrality measure capturing biologically important proteins %U http://dx.doi.org/10.1039/c5mb00732a %X Topological centrality in protein interaction networks and its biological implications has widely been investigated in the past. In the present study, a novel centrality metric - weighted sum of loads eigenvector centrality (WSL-EC) - based on graph spectra was defined and its performance in identifying topologically and biologically important nodes was comparatively investigated with common centrality metrics in human protein-protein interaction network. The metric can capture nodes from peripherals of the network differently from conventional eigenvector centrality. Different metrics were found to selectively identify hub sets significantly associated with different biological processes. Widely accepted metrics; degree centrality, betweenness centrality, subgraph centrality and eigenvector centrality are subject to a bias towards super-hubs while WSL-EC was not affected by the presence of super-hubs. WSL-EC outperforms other centrality metrics in detecting biologically central nodes such as pathogen interacting, cancer, ageing, HIV-1 or disease related proteins and proteins involved in immune system process and autoimmune diseases in human interactome.

@article{Karabekmez2015Novel, abstract = {Topological centrality in protein interaction networks and its biological implications has widely been investigated in the past. In the present study, a novel centrality metric - weighted sum of loads eigenvector centrality ({WSL}-{EC}) - based on graph spectra was defined and its performance in identifying topologically and biologically important nodes was comparatively investigated with common centrality metrics in human protein-protein interaction network. The metric can capture nodes from peripherals of the network differently from conventional eigenvector centrality. Different metrics were found to selectively identify hub sets significantly associated with different biological processes. Widely accepted metrics; degree centrality, betweenness centrality, subgraph centrality and eigenvector centrality are subject to a bias towards super-hubs while {WSL}-{EC} was not affected by the presence of super-hubs. {WSL}-{EC} outperforms other centrality metrics in detecting biologically central nodes such as pathogen interacting, cancer, ageing, {HIV}-1 or disease related proteins and proteins involved in immune system process and autoimmune diseases in human interactome.}, added-at = {2018-12-02T16:09:07.000+0100}, author = {Karabekmez, Muhammed E. and Kirdar, Betul}, biburl = {https://www.bibsonomy.org/bibtex/2b7bc3b5e51c6baa1121212b49440fb15/karthikraman}, citeulike-article-id = {13879355}, citeulike-linkout-0 = {http://dx.doi.org/10.1039/c5mb00732a}, citeulike-linkout-1 = {http://www.rsc.org/Publishing/Journals/article.asp?doi=C5MB00732A}, doi = {10.1039/c5mb00732a}, interhash = {ecc757715e973c2f034258cb3812a553}, intrahash = {b7bc3b5e51c6baa1121212b49440fb15}, journal = {Mol. BioSyst.}, keywords = {centrality networks}, posted-at = {2015-12-10 06:36:38}, priority = {2}, publisher = {The Royal Society of Chemistry}, timestamp = {2018-12-02T16:09:07.000+0100}, title = {A novel topological centrality measure capturing biologically important proteins}, url = {http://dx.doi.org/10.1039/c5mb00732a}, year = 2015 }