IDEAS home Printed from https://ideas.repec.org/a/eee/infome/v12y2018i3p893-905.html
   My bibliography  Save this article

Topology-driven trend analysis for drug discovery

Author

Listed:
  • Lv, Yanhua
  • Ding, Ying
  • Song, Min
  • Duan, Zhiguang

Abstract

The primary goal of the present study is to discover new drug treatments by topology analysis of drug associations and their therapeutic group network. To this end, we collected 19,869 papers dated from 1946 to 2015 that are related to autism treatment from PubMed. We extracted 145 drugs based on MeSH terms and their synonyms (the total number is 6624) within the same ATC classification hierarchy and used them to find drug associations in the collected datasets. We introduced a new topology-driven method that incorporates various network analyses including co-word network, clique percolation, weak component, pathfinding-based analysis of therapeutic groups, and detection of important drug interaction within a clique. The present study showed that the in-depth analysis of the drug relationships extracted from the literature-based network sheds new light on drug discovery research. The results also suggested that certain drugs could be repurposed for autism treatment in the future. In particular, the results indicated that the discovered four drugs such as Tocilizumab, Tacrolimus, Prednisone, and Sulfisoxazole are worthy of further study in laboratory experiments with formal assessment of possible effects on symptoms, which may provide psychologists, physicians, and researchers with data-based scientific hypotheses in autism-drug discovery.

Suggested Citation

  • Lv, Yanhua & Ding, Ying & Song, Min & Duan, Zhiguang, 2018. "Topology-driven trend analysis for drug discovery," Journal of Informetrics, Elsevier, vol. 12(3), pages 893-905.
    • Handle: RePEc:eee:infome:v:12:y:2018:i:3:p:893-905
    DOI: 10.1016/j.joi.2018.07.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1751157718300920
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.joi.2018.07.007?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. Johannes Stegmann & Guenter Grohmann, 2003. "Hypothesis generation guided by co-word clustering," Scientometrics, Springer;Akadémiai Kiadó, vol. 56(1), pages 111-135, January.
    2. Ronald N. Kostoff, 2014. "Literature-related discovery: common factors for Parkinson’s Disease and Crohn’s Disease," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 623-657, September.
    3. Song, Min & Heo, Go Eun & Ding, Ying, 2015. "SemPathFinder: Semantic path analysis for discovering publicly unknown knowledge," Journal of Informetrics, Elsevier, vol. 9(4), pages 686-703.
    4. Loet Leydesdorff & Daniele Rotolo & Ismael Rafols, 2012. "Bibliometric perspectives on medical innovation using the medical subject Headings of PubMed," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(11), pages 2239-2253, November.
    5. Chen, Chaomei & Chen, Yue & Horowitz, Mark & Hou, Haiyan & Liu, Zeyuan & Pellegrino, Donald, 2009. "Towards an explanatory and computational theory of scientific discovery," Journal of Informetrics, Elsevier, vol. 3(3), pages 191-209.
    6. Bin Chen & Ying Ding & David J Wild, 2012. "Assessing Drug Target Association Using Semantic Linked Data," PLOS Computational Biology, Public Library of Science, vol. 8(7), pages 1-10, July.
    7. Toivonen, Riitta & Onnela, Jukka-Pekka & Saramäki, Jari & Hyvönen, Jörkki & Kaski, Kimmo, 2006. "A model for social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 371(2), pages 851-860.
    8. Don R. Swanson, 1987. "Two medical literatures that are logically but not bibliographically connected," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 38(4), pages 228-233, July.
    9. Jeong, Yoo Kyung & Heo, Go Eun & Kang, Keun Young & Yoon, Dong Sup & Song, Min, 2016. "Trajectory analysis of drug-research trends in pancreatic cancer on PubMed and ClinicalTrials.gov," Journal of Informetrics, Elsevier, vol. 10(1), pages 273-285.
    10. Michael D. Gordon & Robert K. Lindsay, 1996. "Toward discovery support systems: A replication, re‐examination, and extension of Swanson's work on literature‐based discovery of a connection between Raynaud's and fish oil," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 47(2), pages 116-128, February.
    11. Peder Olesen Larsen & Markus Ins, 2010. "The rate of growth in scientific publication and the decline in coverage provided by Science Citation Index," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(3), pages 575-603, September.
    12. Small, Henry & Tseng, Hung & Patek, Mike, 2017. "Discovering discoveries: Identifying biomedical discoveries using citation contexts," Journal of Informetrics, Elsevier, vol. 11(1), pages 46-62.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yang, Jinqing & Liu, Zhifeng, 2022. "The effect of citation behaviour on knowledge diffusion and intellectual structure," Journal of Informetrics, Elsevier, vol. 16(1).
    2. Min, Chao & Bu, Yi & Sun, Jianjun, 2021. "Predicting scientific breakthroughs based on knowledge structure variations," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
    3. Houqiang Yu & Yian Liang & Yinghua Xie, 2024. "Predicting Scientific Breakthroughs Based on Structural Dynamic of Citation Cascades," Mathematics, MDPI, vol. 12(11), pages 1-18, June.

    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. Andrej Kastrin & Dimitar Hristovski, 2021. "Scientometric analysis and knowledge mapping of literature-based discovery (1986–2020)," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1415-1451, February.
    2. Jeong, Yoo Kyung & Xie, Qing & Yan, Erjia & Song, Min, 2020. "Examining drug and side effect relation using author–entity pair bipartite networks," Journal of Informetrics, Elsevier, vol. 14(1).
    3. Daniele Rotolo & Loet Leydesdorff, 2014. "Matching MEDLINE/PubMed Data with Web of Science (WOS): A Routine in R language," SPRU Working Paper Series 2014-14, SPRU - Science Policy Research Unit, University of Sussex Business School.
    4. Christian Sternitzke, 2009. "Patents and publications as sources of novel and inventive knowledge," Scientometrics, Springer;Akadémiai Kiadó, vol. 79(3), pages 551-561, June.
    5. Chaker Jebari & Enrique Herrera-Viedma & Manuel Jesus Cobo, 2021. "The use of citation context to detect the evolution of research topics: a large-scale analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 2971-2989, April.
    6. Min, Chao & Bu, Yi & Sun, Jianjun, 2021. "Predicting scientific breakthroughs based on knowledge structure variations," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
    7. Chen, Chaomei & Chen, Yue & Horowitz, Mark & Hou, Haiyan & Liu, Zeyuan & Pellegrino, Donald, 2009. "Towards an explanatory and computational theory of scientific discovery," Journal of Informetrics, Elsevier, vol. 3(3), pages 191-209.
    8. Liang, Guoqiang & Hou, Haiyan & Ding, Ying & Hu, Zhigang, 2020. "Knowledge recency to the birth of Nobel Prize-winning articles: Gender, career stage, and country," Journal of Informetrics, Elsevier, vol. 14(3).
    9. Xue Wang & Xuemei Yang & Jian Du & Xuwen Wang & Jiao Li & Xiaoli Tang, 2021. "A deep learning approach for identifying biomedical breakthrough discoveries using context analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5531-5549, July.
    10. Jacob Wood & Gohar Feroz Khan, 2015. "International trade negotiation analysis: network and semantic knowledge infrastructure," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(1), pages 537-556, October.
    11. Stephen Carley & Alan L. Porter, 2012. "A forward diversity index," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(2), pages 407-427, February.
    12. Lutz Bornmann & Robin Haunschild & Sven E. Hug, 2018. "Visualizing the context of citations referencing papers published by Eugene Garfield: a new type of keyword co-occurrence analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(2), pages 427-437, February.
    13. Senator Jeong & Hong-Gee Kim, 2010. "Intellectual structure of biomedical informatics reflected in scholarly events," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(2), pages 541-551, November.
    14. Petersen, Alexander M. & Rotolo, Daniele & Leydesdorff, Loet, 2016. "A triple helix model of medical innovation: Supply, demand, and technological capabilities in terms of Medical Subject Headings," Research Policy, Elsevier, vol. 45(3), pages 666-681.
    15. Gao, Qiang & Liang, Zhentao & Wang, Ping & Hou, Jingrui & Chen, Xiuxiu & Liu, Manman, 2021. "Potential index: Revealing the future impact of research topics based on current knowledge networks," Journal of Informetrics, Elsevier, vol. 15(3).
    16. Souzanchi Kashani, Ebrahim & Roshani, Saeed, 2019. "Evolution of innovation system literature: Intellectual bases and emerging trends," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 68-80.
    17. Tian Wang & Zhaoping Yang & Xiaodong Chen & Fang Han, 2022. "Bibliometric Analysis and Literature Review of Tourism Destination Resilience Research," IJERPH, MDPI, vol. 19(9), pages 1-16, May.
    18. Tuan V. Nguyen & Ly T. Pham, 2011. "Scientific output and its relationship to knowledge economy: an analysis of ASEAN countries," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(1), pages 107-117, October.
    19. Xian Li & Ronald Rousseau & Liming Liang & Fangjie Xi & Yushuang Lü & Yifan Yuan & Xiaojun Hu, 2022. "Is low interdisciplinarity of references an unexpected characteristic of Nobel Prize winning research?," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 2105-2122, April.
    20. Ruhua Huang & Yuting Huang & Fan Qi & Leyi Shi & Baiyang Li & Wei Yu, 2022. "Exploring the characteristics of special issues: distribution, topicality, and citation impact," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5233-5256, September.

    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:infome:v:12:y:2018:i:3:p:893-905. 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: http://www.elsevier.com/locate/joi .

    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.