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Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 BMC+Bioinformatics 2016 ; 17 (1): ä Nephropedia Template TP
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Fusing literature and full network data improves disease similarity computation #MMPMID27578323
Li P; Nie Y; Yu J
BMC Bioinformatics 2016[]; 17 (1): ä PMID27578323show ga
Background: Identifying relatedness among diseases could help deepen understanding for the underlying pathogenic mechanisms of diseases, and facilitate drug repositioning projects. A number of methods for computing disease similarity had been developed; however, none of them were designed to utilize information of the entire protein interaction network, using instead only those interactions involving disease causing genes. Most of previously published methods required gene-disease association data, unfortunately, many diseases still have very few or no associated genes, which impeded broad adoption of those methods. In this study, we propose a new method (MedNetSim) for computing disease similarity by integrating medical literature and protein interaction network. MedNetSim consists of a network-based method (NetSim), which employs the entire protein interaction network, and a MEDLINE-based method (MedSim), which computes disease similarity by mining the biomedical literature. Results: Among function-based methods, NetSim achieved the best performance. Its average AUC (area under the receiver operating characteristic curve) reached 95.2 %. MedSim, whose performance was even comparable to some function-based methods, acquired the highest average AUC in all semantic-based methods. Integration of MedSim and NetSim (MedNetSim) further improved the average AUC to 96.4 %. We further studied the effectiveness of different data sources. It was found that quality of protein interaction data was more important than its volume. On the contrary, higher volume of gene-disease association data was more beneficial, even with a lower reliability. Utilizing higher volume of disease-related gene data further improved the average AUC of MedNetSim and NetSim to 97.5 % and 96.7 %, respectively. Conclusions: Integrating biomedical literature and protein interaction network can be an effective way to compute disease similarity. Lacking sufficient disease-related gene data, literature-based methods such as MedSim can be a great addition to function-based algorithms. It may be beneficial to steer more resources torward studying gene-disease associations and improving the quality of protein interaction data. Disease similarities can be computed using the proposed methods at http://www.digintelli.com:8000/. Electronic supplementary material: The online version of this article (doi:10.1186/s12859-016-1205-4) contains supplementary material, which is available to authorized users.
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BMC+Bioinformatics 2016 ; 17 (1): ä
