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Wikipedia network analysis of cancer interactions and world influence

Author

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  • Guillaume Rollin
  • José Lages
  • Dima L Shepelyansky

Abstract

We apply the Google matrix algorithms for analysis of interactions and influence of 37 cancer types, 203 cancer drugs and 195 world countries using the network of 5 416 537 English Wikipedia articles with all their directed hyperlinks. The PageRank algorithm provides a ranking of cancers which has 60% and 70% overlaps with the top 10 deadliest cancers extracted from World Health Organization GLOBOCAN 2018 and Global Burden of Diseases Study 2017, respectively. The recently developed reduced Google matrix algorithm gives networks of interactions between cancers, drugs and countries taking into account all direct and indirect links between these selected 435 entities. These reduced networks allow to obtain sensitivity of countries to specific cancers and drugs. The strongest links between cancers and drugs are in good agreement with the approved medical prescriptions of specific drugs to specific cancers. We argue that this analysis of knowledge accumulated in Wikipedia provides useful complementary global information about interdependencies between cancers, drugs and world countries.

Suggested Citation

  • Guillaume Rollin & José Lages & Dima L Shepelyansky, 2019. "Wikipedia network analysis of cancer interactions and world influence," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-26, September.
    • Handle: RePEc:plo:pone00:0222508
    DOI: 10.1371/journal.pone.0222508
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    References listed on IDEAS

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    1. Célestin Coquidé & Leonardo Ermann & José Lages & Dima L. Shepelyansky, 2019. "Influence of petroleum and gas trade on EU economies from the reduced Google matrix analysis of UN COMTRADE data," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 92(8), pages 1-14, August.
    2. A. O. Zhirov & O. V. Zhirov & D. L. Shepelyansky, 2010. "Two-dimensional ranking of Wikipedia articles," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 77(4), pages 523-531, October.
    3. C'elestin Coquid'e & Leonardo Ermann & Jos'e Lages & D. L. Shepelyansky, 2019. "Influence of petroleum and gas trade on EU economies from the reduced Google matrix analysis of UN COMTRADE data," Papers 1903.01820, arXiv.org.
    4. C'elestin Coquid'e & Jos'e Lages & Dima L. Shepelyansky, 2019. "Interdependence of sectors of economic activities for world countries from the reduced Google matrix analysis of WTO data," Papers 1905.06489, arXiv.org, revised Jun 2019.
    5. Samer El Zant & Katia Jaffrès-Runser & Dima L Shepelyansky, 2018. "Capturing the influence of geopolitical ties from Wikipedia with reduced Google matrix," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-31, August.
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