IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v164y2021ics0040162520313391.html
   My bibliography  Save this article

Exploring the genetic basis of diseases through a heterogeneous bibliometric network: A methodology and case study

Author

Listed:
  • Wu, Mengjia
  • Zhang, Yi
  • Zhang, Guangquan
  • Lu, Jie

Abstract

Literature-based knowledge (LBD) discovery is a practical approach to inferring the associations between diseases and genetic factors from unstructured biomedical data, i.e., the literature. However, most of the contemporary LBD methods are designed for specific cases and rely heavily on prior knowledge. In this paper, we propose an adaptable and transferable methodology that not only summarizes the genetic factors known to be associated with a queried disease but also predicts likely associations that have yet to be identified. The framework incorporates different biomedical entities in a heterogeneous co-occurrence network. Three centrality indicators, coupled with a novel measure based on intersection ratios, capture the importance and specificity of each factor to the disease under study. Undiscovered, but likely, associations are identified through a semantic similarity matrix generated by our Bioentity2Vec model and an innovative weighted link prediction algorithm. The final outputs are ranked lists of the most relevant known or potential biomedical associations. To both test and showcase the methodology, we conducted a case study on atrial fibrillation. The analysis yields specific insights into the key biomedical entities associated with this disease. Moreover, it demonstrates the kind of valuable decision support this framework can provide to medical researchers, policymakers and public health administrations.

Suggested Citation

  • Wu, Mengjia & Zhang, Yi & Zhang, Guangquan & Lu, Jie, 2021. "Exploring the genetic basis of diseases through a heterogeneous bibliometric network: A methodology and case study," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
  • Handle: RePEc:eee:tefoso:v:164:y:2021:i:c:s0040162520313391
    DOI: 10.1016/j.techfore.2020.120513
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162520313391
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.techfore.2020.120513?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. Yi Zhang & Yue Qian & Ying Huang & Ying Guo & Guangquan Zhang & Jie Lu, 2017. "An entropy-based indicator system for measuring the potential of patents in technological innovation: rejecting moderation," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1925-1946, June.
    2. Zhang, Yi & Lu, Jie & Liu, Feng & Liu, Qian & Porter, Alan & Chen, Hongshu & Zhang, Guangquan, 2018. "Does deep learning help topic extraction? A kernel k-means clustering method with word embedding," Journal of Informetrics, Elsevier, vol. 12(4), pages 1099-1117.
    3. Yan-yan Li & Chuan-wei Zhou & Jian Xu & Yun Qian & Bei Wang, 2012. "CYP11B2 T-344C Gene Polymorphism and Atrial Fibrillation: A Meta-Analysis of 2,758 Subjects," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-7, November.
    4. Christopher E. Lawson & Sha Wu & Ananda S. Bhattacharjee & Joshua J. Hamilton & Katherine D. McMahon & Ramesh Goel & Daniel R. Noguera, 2017. "Metabolic network analysis reveals microbial community interactions in anammox granules," Nature Communications, Nature, vol. 8(1), pages 1-12, August.
    5. Xavier Clemente-Casares & Jesus Blanco & Poornima Ambalavanan & Jun Yamanouchi & Santiswarup Singha & Cesar Fandos & Sue Tsai & Jinguo Wang & Nahir Garabatos & Cristina Izquierdo & Smriti Agrawal & Mi, 2016. "Expanding antigen-specific regulatory networks to treat autoimmunity," Nature, Nature, vol. 530(7591), pages 434-440, February.
    6. Zhang, Yi & Zhang, Guangquan & Chen, Hongshu & Porter, Alan L. & Zhu, Donghua & Lu, Jie, 2016. "Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 179-191.
    7. Tao Zhou & Linyuan Lü & Yi-Cheng Zhang, 2009. "Predicting missing links via local information," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(4), pages 623-630, October.
    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. Richarz, Jan & Wegewitz, Stephan & Henn, Sarah & Müller, Dirk, 2023. "Graph-based research field analysis by the use of natural language processing: An overview of German energy research," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).

    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. Zhang, Yi & Wu, Mengjia & Miao, Wen & Huang, Lu & Lu, Jie, 2021. "Bi-layer network analytics: A methodology for characterizing emerging general-purpose technologies," Journal of Informetrics, Elsevier, vol. 15(4).
    2. Gao, Xue & Zhang, Yi, 2022. "What is behind the globalization of technology? Exploring the interplay of multi-level drivers of international patent extension in the solar photovoltaic industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    3. Juhyun Lee & Sangsung Park & Junseok Lee, 2023. "Exploring Potential R&D Collaboration Partners Using Embedding of Patent Graph," Sustainability, MDPI, vol. 15(20), pages 1-19, October.
    4. Yi Zhang & Xiaojing Cai & Caroline V. Fry & Mengjia Wu & Caroline S. Wagner, 2021. "Topic evolution, disruption and resilience in early COVID-19 research," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4225-4253, May.
    5. Huang, Lu & Chen, Xiang & Ni, Xingxing & Liu, Jiarun & Cao, Xiaoli & Wang, Changtian, 2021. "Tracking the dynamics of co-word networks for emerging topic identification," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    6. Lu Huang & Xiang Chen & Yi Zhang & Yihe Zhu & Suyi Li & Xingxing Ni, 2021. "Dynamic network analytics for recommending scientific collaborators," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 8789-8814, November.
    7. Tingcan Ma & Ruinan Li & Guiyan Ou & Mingliang Yue, 2018. "Topic based research competitiveness evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(2), pages 789-803, November.
    8. Zhang, Yi & Huang, Ying & Porter, Alan L. & Zhang, Guangquan & Lu, Jie, 2019. "Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 795-807.
    9. Thanh Loc Nguyen & Youngjin Choi & Jihye Im & Hyunsu Shin & Ngoc Man Phan & Min Kyung Kim & Seung Woo Choi & Jaeyun Kim, 2022. "Immunosuppressive biomaterial-based therapeutic vaccine to treat multiple sclerosis via re-establishing immune tolerance," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    10. Karimi, Fatemeh & Lotfi, Shahriar & Izadkhah, Habib, 2021. "Community-guided link prediction in multiplex networks," Journal of Informetrics, Elsevier, vol. 15(4).
    11. Park, Jinhee & Ahn, Hyeongjin & Kim, Dongjae & Park, Eunil, 2024. "GNN-IR: Examining graph neural networks for influencer recommendations in social media marketing," Journal of Retailing and Consumer Services, Elsevier, vol. 78(C).
    12. Li Li & Cwyn Solvi & Feng Zhang & Zhaoyang Qi & Lars Chittka & Wei Zhao, 2021. "Gut microbiome drives individual memory variation in bumblebees," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    13. Shang, Ronghua & Zhang, Weitong & Jiao, Licheng & Stolkin, Rustam & Xue, Yu, 2017. "A community integration strategy based on an improved modularity density increment for large-scale networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 471-485.
    14. Lin, Dan & Wu, Jiajing & Xuan, Qi & Tse, Chi K., 2022. "Ethereum transaction tracking: Inferring evolution of transaction networks via link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    15. Chen, Ling-Jiao & Zhang, Zi-Ke & Liu, Jin-Hu & Gao, Jian & Zhou, Tao, 2017. "A vertex similarity index for better personalized recommendation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 607-615.
    16. Hyejin Park & Han Woo Park, 2018. "Two-side face of knowledge building using scientometric analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(6), pages 2815-2836, November.
    17. Ariel Isser & Aliyah B. Silver & Hawley C. Pruitt & Michal Mass & Emma H. Elias & Gohta Aihara & Si-Sim Kang & Niklas Bachmann & Ying-Yu Chen & Elissa K. Leonard & Joan G. Bieler & Worarat Chaisawangw, 2022. "Nanoparticle-based modulation of CD4+ T cell effector and helper functions enhances adoptive immunotherapy," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    18. Channakeshava Sokke Umeshappa & Patricia Solé & Jun Yamanouchi & Saswat Mohapatra & Bas G. J. Surewaard & Josep Garnica & Santiswarup Singha & Debajyoti Mondal & Elena Cortés-Vicente & Charlotte D’Mel, 2022. "Re-programming mouse liver-resident invariant natural killer T cells for suppressing hepatic and diabetogenic autoimmunity," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    19. Andreas Spitz & Anna Gimmler & Thorsten Stoeck & Katharina Anna Zweig & Emőke-Ágnes Horvát, 2016. "Assessing Low-Intensity Relationships in Complex Networks," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-17, April.
    20. Ricardo S. Santos & Jose Soares & Pedro Carmona Marques & Helena V. G. Navas & José Moleiro Martins, 2021. "Integrating Business, Social, and Environmental Goals in Open Innovation through Partner Selection," Sustainability, MDPI, vol. 13(22), pages 1-25, November.

    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:tefoso:v:164:y:2021:i:c:s0040162520313391. 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.sciencedirect.com/science/journal/00401625 .

    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.