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Relating Diseases Based on Disease Module Theory

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Bioinformatics Research and Applications (ISBRA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10330))

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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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Authors and Affiliations

  1. School of Information Science and Engineering, Central South University, Changsha, 410083, China

    Peng Ni, Min Li, Ping Zhong, Guihua Duan, Jianxin Wang & FangXiang Wu

  2. Department of Computer Science, Old Dominion University, Norfolk, VA, 23529, USA

    Yaohang Li

  3. Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SKS7N5A9, Canada

    FangXiang Wu

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  1. Peng Ni
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  2. Min Li
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  4. Guihua Duan
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  5. Jianxin Wang
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  7. FangXiang Wu
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Correspondence to Min Li .

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Editors and Affiliations

  1. Georgia State University, Atlanta, Georgia, USA

    Zhipeng Cai

  2. University of Texas at Dallas, Richardson, Texas, USA

    Ovidiu Daescu

  3. Central South University, Changsha, China

    Min Li

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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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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-59575-7

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