Abstract
Understanding disease-disease associations can not only help us gain deeper insights into complex diseases, but also lead to improvements in disease diagnosis, drug repositioning and new drug development. Due to the growing body of high-throughput biological data, a number of methods have been proposed for the computation of similarity among diseases during past decades. Recently, the disease module theory has been presented, which states that disease-related genes or proteins tend to interact with each other in the same neighborhood of protein-protein interaction network. In this study, we propose a new method called ModuleSim to measure associations between diseases by using disease-gene association data and protein-protein interaction network data based on disease module theory. By considering the interactions between disease modules and each module’s modularity, ModuleSim outperforms other four popular methods for predicting disease-disease similarity.
This work is supported by the National Science Fund for Excellent Young Scholars under Grant No. 61622213, the National Natural Science Foundation of China under grant No. 61370024 and No. 61472133, and the Program of Independent Exploration Innovation in Central South University (2016zzts354).
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References
Vanunu, O., Magger, O., Ruppin, E., et al.: Associating genes and protein complexes with disease via network propagation. PLoS Comput. Biol. 6(1), e1000641 (2010)
Li, M., Zheng, R., Li, Q., et al.: Prioritizing disease genes by using search engine algorithm. Curr. Bioinform. 11(2), 195–202 (2016)
Luo, H., Wang, J., Li, M., et al.: Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm. Bioinformatics 32(17), 2664–2671 (2016)
van Driel, M.A., Bruggeman, J., Vriend, G., et al.: A text-mining analysis of the huamn phenome. Eur. J. Hum. Genet. 14(5), 535–542 (2006)
Goh, K.I., Cusick, M.E., Valle, D., et al.: The human disease network. Proc. Natl. Acad. Sci. 104(21), 8685–8690 (2007)
Jung, J., Lee, D.: Inferring disease association using clinical factors in a combinatorial manner and their use in drug repositioning. Bioinformatics 29(16), 2017–2023 (2013)
Sun, K., Buchan, N., Larminie, C., et al.: The integrated disease network. Integr. Biol. 6(11), 1069–1079 (2014)
Frick, J.M., Guha, R., Peryea, T., et al.: Evaluating disease similarity using latent Dirichlet allocation. bioRxiv: 030593 (2015)
Cheng, L., Li, J., Ju, P., et al.: SemFunSim: a new method for measuring disease similarity by integrating semantic and gene functional association. PLoS One 9(6), e99415 (2014)
Schriml, L.M., et al.: Disease ontology: a backbone for disease semantic integration. Nucleic Acids Res. 40(D1), D940–D946 (2012)
Lipscomb, C.E.: Medical subject headings (MeSH). Bull. Med. Libr. Assoc. 88(3), 265 (2000)
Yu, G., Wang, L.G., Yan, G.R., et al.: DOSE: an R/Bioconductor package for disease ontology semantic and enrichment analysis. Bioinformatics 31(4), 608–609 (2015)
Wang, D., Wang, J., Lu, M., et al.: Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases. Bioinformatics 26(13), 1644–1650 (2010)
Zhang, X., Zhang, R., Jiang, Y., et al.: The expanded human disease network combining protein–protein interaction information. Eur. J. Hum. Genet. 19(7), 783–788 (2011)
Mathur, S., Dinakarpandian, D.: Automated ontological gene annotation for computing disease similarity. AMIA Summits Transl. Sci. Proc. 2010, 12–16 (2010)
Mathur, S., Dinakarpandian, D.: Finding disease similarity based on implicit semantic similarity. J. Biomed. Inform. 45(2), 363–371 (2012)
Ashburner, M., Ball, C.A., Blake, J.A., et al.: Gene ontology: tool for the unification of biology. Nat. Genet. 25(1), 25–29 (2000)
Sun, K., Gonçalves, J.P., Larminie, C., et al.: Predicting disease associations via biological network analysis. BMC Bioinform. 15(1), 1 (2014)
Milenkoviæ, T., Pržulj, N.: Uncovering biological network function via graphlet degree signatures. Cancer Inform. 6, 257 (2008)
Hamaneh, M.B., Yu, Y.K.: Relating diseases by integrating gene associations and information flow through protein interaction network. PLoS ONE 9(10), e110936 (2014)
Li, P., Nie, Y., Yu, J.: Fusing literature and full network data improves disease similarity computation. BMC Bioinform. 17(1), 326 (2016)
Köhler, S., Bauer, S., Horn, D., et al.: Walking the interactome for prioritization of candidate disease genes. Am. J. Hum. Genet. 82(4), 949–958 (2008)
Menche, J., Sharma, A., Kitsak, M., et al.: Uncovering disease-disease relationships through the incomplete interactome. Science 347(6224), 1257601 (2015)
Piñero, J., Queralt-Rosinach, N., Bravo, À., et al.: DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database 2015, bav028 (2015)
Cheng, L., Wang, G., Li, J., et al.: SIDD: a semantically integrated database towards a global view of human disease. PLoS ONE 8(10), e75504 (2013)
Chatr-Aryamontri, A., Breitkreutz, B.J., Heinicke, S., et al.: The BioGRID interaction database: 2013 update. Nucleic Acids Res. 41(D1), D816–D823 (2013)
Prasad, T.S.K., Goel, R., Kandasamy, K., et al.: Human protein reference database—2009 update. Nucleic Acids Res. 37(suppl 1), D767–D772 (2009)
Orchard, S., Ammari, M., Aranda, B., et al.: The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res. 42(D1), D358–D363 (2013)
Persico, M., Ceol, A., Gavrila, C., et al.: HomoMINT: an inferred human network based on orthology mapping of protein interactions discovered in model organisms. BMC Bioinform. 6(4), 1 (2005)
Suthram, S., Dudley, J.T., Chiang, A.P., et al.: Network-based elucidation of human disease similarities reveals common functional modules enriched for pluripotent drug targets. PLoS Comput. Biol. 6(2), e1000662 (2010)
Pakhomov, S., McInnes, B., Adam, T., et al.: Semantic similarity and relatedness between clinical terms: an experimental study. In: AMIA annual symposium proceedings. American Medical Informatics Association, p. 572 (2010)
Lee, I., Blom, U.M., Wang, P.I., et al.: Prioritizing candidate disease genes by network-based boosting of genome-wide association data. Genome Res. 21(7), 1109–1121 (2011)
Ni, J., Koyuturk, M., Tong, H., et al.: Disease gene prioritization by integrating tissue-specific molecular networks using a robust multi-network model. BMC Bioinform. 17(1), 453 (2016)
Mitchell, J.A., Aronson, A.R., Mork, J.G., et al.: Gene indexing: characterization and analysis of NLM’s GeneRIFs. In: AMIA (2003)
Amberger, J.S., Bocchini, C.A., Schiettecatte, F., et al.: OMIM.org: Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders. Nucleic Acids Res. 43(D1), D789–D798 (2015)
Davis, A.P., Murphy, C.G., Johnson, R., et al.: The comparative toxicogenomics database: update 2013. Nucleic Acids Res. 41(D1), D1104–D1114 (2012)
Becker, K.G., Barnes, K.C., Bright, T.J., et al.: The genetic association database. Nat. Genet. 36(5), 431–432 (2004)
Wang, J., Zhang, J., Li, K., et al.: SpliceDisease database: linking RNA splicing and disease. Nucleic Acids Res. 40(D1), D1055–D1059 (2012)
Bodenreider, O.: The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32(suppl 1), D267–D270 (2004)
Barabási, A.L., Gulbahce, N., Loscalzo, J.: Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12(1), 56–68 (2011)
Guo, X., Zhang, J., Cai, Z., et al.: Searching genome-wide multi-locus associations for multiple diseases based on Bayesian Inference. In: IEEE/ACM transactions on computational biology and bioinformatics (2016)
Teng, B., Yang, C., Liu, J., et al.: Exploring the genetic patterns of complex diseases via the integrative genome-wide approach. IEEE/ACM Trans. Comput. Biol. Bioinform. 13(3), 557–564 (2016)
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Ni, P. et al. (2017). Relating Diseases Based on Disease Module Theory. In: Cai, Z., Daescu, O., Li, M. (eds) Bioinformatics Research and Applications. ISBRA 2017. Lecture Notes in Computer Science(), vol 10330. Springer, Cham. https://doi.org/10.1007/978-3-319-59575-7_3
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DOI: https://doi.org/10.1007/978-3-319-59575-7_3
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