IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v395y2021ics0096300320308237.html
   My bibliography  Save this article

Centrality analysis in a drug network and its application to drug repositioning

Author

Listed:
  • Keng, Ying Ying
  • Kwa, Kiam Heong
  • Ratnavelu, Kurunathan

Abstract

Centrality measures play a vital role in network analysis by which important nodes within a network are identified from structural perspectives. In this study, we applied three fundamental centrality measures (degree, closeness, and betweenness) to analyze a drug network where drugs are connected based on their side-effect similarities. The results suggest that centralities of drugs in the network may have a significant implication in drug repositioning – a process of discovering new therapeutic uses of existing drugs. Given a particular disease, the drugs that have been approved for treating it were ranked by their centralities. It is shown that the top central ones among them are more likely to repurpose their neighboring drugs as new treatment options for the disease, as compared to their random and peripheral counterparts. Our predictions have proved to be in line with clinical interests indicated by the existing clinical studies in ClinicalTrials.gov database. The present work offers novel insights into complementing drug repositioning efforts while portraying the significance of network centrality measures in guiding systematic analysis for a successful network application.

Suggested Citation

  • Keng, Ying Ying & Kwa, Kiam Heong & Ratnavelu, Kurunathan, 2021. "Centrality analysis in a drug network and its application to drug repositioning," Applied Mathematics and Computation, Elsevier, vol. 395(C).
    • Handle: RePEc:eee:apmaco:v:395:y:2021:i:c:s0096300320308237
    DOI: 10.1016/j.amc.2020.125870
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300320308237
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2020.125870?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Li, Chao & Wang, Li & Sun, Shiwen & Xia, Chengyi, 2018. "Identification of influential spreaders based on classified neighbors in real-world complex networks," Applied Mathematics and Computation, Elsevier, vol. 320(C), pages 512-523.
    2. Wang, Juan & Li, Chao & Xia, Chengyi, 2018. "Improved centrality indicators to characterize the nodal spreading capability in complex networks," Applied Mathematics and Computation, Elsevier, vol. 334(C), pages 388-400.
    3. Yousoff Effendy Mohd Ali & Kiam Heong Kwa & Kurunathan Ratnavelu, 2017. "Predicting new drug indications from network analysis," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 28(09), pages 1-19, September.
    4. DiMasi, Joseph A. & Hansen, Ronald W. & Grabowski, Henry G., 2003. "The price of innovation: new estimates of drug development costs," Journal of Health Economics, Elsevier, vol. 22(2), pages 151-185, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jiang, Lincheng & Zhao, Xiang & Ge, Bin & Xiao, Weidong & Ruan, Yirun, 2019. "An efficient algorithm for mining a set of influential spreaders in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 58-65.
    2. Zareie, Ahmad & Sheikhahmadi, Amir, 2019. "EHC: Extended H-index Centrality measure for identification of users’ spreading influence in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 141-155.
    3. Wang, Zhishuang & Guo, Quantong & Sun, Shiwen & Xia, Chengyi, 2019. "The impact of awareness diffusion on SIR-like epidemics in multiplex networks," Applied Mathematics and Computation, Elsevier, vol. 349(C), pages 134-147.
    4. Xu, Paiheng & Zhang, Rong & Deng, Yong, 2018. "A novel visibility graph transformation of time series into weighted networks," Chaos, Solitons & Fractals, Elsevier, vol. 117(C), pages 201-208.
    5. Li, Huichun & Zhang, Xue & Zhao, Chengli, 2021. "Explaining social events through community evolution on temporal networks," Applied Mathematics and Computation, Elsevier, vol. 404(C).
    6. Agryzkov, Taras & Tortosa, Leandro & Vicent, Jose F., 2019. "A variant of the current flow betweenness centrality and its application in urban networks," Applied Mathematics and Computation, Elsevier, vol. 347(C), pages 600-615.
    7. Wang, Weiping & Guo, Junjiang & Wang, Zhen & Wang, Hao & Cheng, Jun & Wang, Chunyang & Yuan, Manman & Kurths, Jürgen & Luo, Xiong & Gao, Yang, 2021. "Abnormal flow detection in industrial control network based on deep reinforcement learning," Applied Mathematics and Computation, Elsevier, vol. 409(C).
    8. Giacopelli, G. & Migliore, M. & Tegolo, D., 2020. "Graph-theoretical derivation of brain structural connectivity," Applied Mathematics and Computation, Elsevier, vol. 377(C).
    9. Curado, Manuel & Tortosa, Leandro & Vicent, Jose F., 2021. "Identifying mobility patterns by means of centrality algorithms in multiplex networks," Applied Mathematics and Computation, Elsevier, vol. 406(C).
    10. da Cunha, Éverton Fernandes & da Fontoura Costa, Luciano, 2022. "On hypercomplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    11. Zhu, Linhe & Liu, Mengxue & Li, Yimin, 2019. "The dynamics analysis of a rumor propagation model in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 118-137.
    12. Yao, Hongxing & Memon, Bilal Ahmed, 2019. "Network topology of FTSE 100 Index companies: From the perspective of Brexit," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1248-1262.
    13. Liu, Wanping & Wu, Xiao & Yang, Wu & Zhu, Xiaofei & Zhong, Shouming, 2019. "Modeling cyber rumor spreading over mobile social networks: A compartment approach," Applied Mathematics and Computation, Elsevier, vol. 343(C), pages 214-229.
    14. Bodaghi, Amirhosein & Goliaei, Sama & Salehi, Mostafa, 2019. "The number of followings as an influential factor in rumor spreading," Applied Mathematics and Computation, Elsevier, vol. 357(C), pages 167-184.
    15. Holger Patzelt & Dean A. Shepherd, 2009. "Strategic Entrepreneurship at Universities: Academic Entrepreneurs’ Assessment of Policy Programs," Entrepreneurship Theory and Practice, , vol. 33(1), pages 319-340, January.
    16. Patzelt, Holger & zu Knyphausen-Aufseß, Dodo & Fischer, Heiko T., 2009. "Upper echelons and portfolio strategies of venture capital firms," Journal of Business Venturing, Elsevier, vol. 24(6), pages 558-572, November.
    17. Dosis, Anastasios & Muthoo, Abhinay, 2019. "Experimentation in Dynamic R&D Competition," CRETA Online Discussion Paper Series 52, Centre for Research in Economic Theory and its Applications CRETA.
    18. Tomas J. Philipson & Eric Sun, 2008. "Is the Food And Drug Administration Safe And Effective?," Journal of Economic Perspectives, American Economic Association, vol. 22(1), pages 85-102, Winter.
    19. Ming Liu & Sumner LaCroix, 2011. "The Impact of Stronger Property Rights in Pharmaceuticals on Innovation in Developed and Developing Countries," Working Papers 201116, University of Hawaii at Manoa, Department of Economics.
    20. Edouard Debonneuil & Anne Eyraud-Loisel & Frédéric Planchet, 2018. "Can Pension Funds Partially Manage Longevity Risk by Investing in a Longevity Megafund?," Risks, MDPI, vol. 6(3), pages 1-27, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:apmaco:v:395:y:2021:i:c:s0096300320308237. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.