IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1002574.html
   My bibliography  Save this article

Assessing Drug Target Association Using Semantic Linked Data

Author

Listed:
  • Bin Chen
  • Ying Ding
  • David J Wild

Abstract

The rapidly increasing amount of public data in chemistry and biology provides new opportunities for large-scale data mining for drug discovery. Systematic integration of these heterogeneous sets and provision of algorithms to data mine the integrated sets would permit investigation of complex mechanisms of action of drugs. In this work we integrated and annotated data from public datasets relating to drugs, chemical compounds, protein targets, diseases, side effects and pathways, building a semantic linked network consisting of over 290,000 nodes and 720,000 edges. We developed a statistical model to assess the association of drug target pairs based on their relation with other linked objects. Validation experiments demonstrate the model can correctly identify known direct drug target pairs with high precision. Indirect drug target pairs (for example drugs which change gene expression level) are also identified but not as strongly as direct pairs. We further calculated the association scores for 157 drugs from 10 disease areas against 1683 human targets, and measured their similarity using a score matrix. The similarity network indicates that drugs from the same disease area tend to cluster together in ways that are not captured by structural similarity, with several potential new drug pairings being identified. This work thus provides a novel, validated alternative to existing drug target prediction algorithms. The web service is freely available at: http://chem2bio2rdf.org/slap. Author Summary: Modern drug discovery requires the understanding of chemogenomics, the complex interaction of chemical compounds and drugs with a wide variety of protein target and genes in the body. A large amount of data pertaining to such relationships exists in publicly-accessible datasets but it is siloed and thus impossible to use in an integrated fashion. In this work we have integrated and semantically annotated a large amount of public data from a wide range of databases, including compound-gene, drug-drug, protein-protein, drug-side effects and so on, to create a complex network of interactions relating to compounds and protein targets. We developed a statistical algorithm called Semantic Link Association Prediction (SLAP) for predicting “missing links” in this data network: i.e. compound-target interactions for which there is no experimental data but which are statistically probable given the other relationships that exist in this set. We present validation experiments which show this method works with a high degree of accuracy, and also demonstrate how it can be used to create a drug similarity network to make predictions of new indications for existing drugs.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pcbi00:1002574
    DOI: 10.1371/journal.pcbi.1002574
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002574
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1002574&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1002574?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
    ---><---

    References listed on IDEAS

    as
    1. Bing He & Jie Tang & Ying Ding & Huijun Wang & Yuyin Sun & Jae Hong Shin & Bin Chen & Ganesh Moorthy & Judy Qiu & Pankaj Desai & David J Wild, 2011. "Mining Relational Paths in Integrated Biomedical Data," PLOS ONE, Public Library of Science, vol. 6(12), pages 1-14, December.
    2. Michael J. Keiser & Vincent Setola & John J. Irwin & Christian Laggner & Atheir I. Abbas & Sandra J. Hufeisen & Niels H. Jensen & Michael B. Kuijer & Roberto C. Matos & Thuy B. Tran & Ryan Whaley & Ri, 2009. "Predicting new molecular targets for known drugs," Nature, Nature, vol. 462(7270), pages 175-181, November.
    3. David Liben‐Nowell & Jon Kleinberg, 2007. "The link‐prediction problem for social networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(7), pages 1019-1031, May.
    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. 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.
    2. Yong Liu & Min Wu & Chunyan Miao & Peilin Zhao & Xiao-Li Li, 2016. "Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction," PLOS Computational Biology, Public Library of Science, vol. 12(2), pages 1-26, February.

    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. Jeong, Yujin & Park, Inchae & Yoon, Byungun, 2019. "Identifying emerging Research and Business Development (R&BD) areas based on topic modeling and visualization with intellectual property right data," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 655-672.
    2. Yifei Zhou & Shaoyong Li & Yaping Liu, 2020. "Graph-based Method for App Usage Prediction with Attributed Heterogeneous Network Embedding," Future Internet, MDPI, vol. 12(3), pages 1-16, March.
    3. Karimi, Fatemeh & Lotfi, Shahriar & Izadkhah, Habib, 2021. "Community-guided link prediction in multiplex networks," Journal of Informetrics, Elsevier, vol. 15(4).
    4. Xu, Hua & Wang, Minggang & Jiang, Shumin & Yang, Weiguo, 2020. "Carbon price forecasting with complex network and extreme learning machine," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    5. 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.
    6. Dong-Sheng Cao & Yi-Zeng Liang & Zhe Deng & Qian-Nan Hu & Min He & Qing-Song Xu & Guang-Hua Zhou & Liu-Xia Zhang & Zi-xin Deng & Shao Liu, 2013. "Genome-Scale Screening of Drug-Target Associations Relevant to Ki Using a Chemogenomics Approach," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-12, April.
    7. Qiaoran Yang & Zhiliang Dong & Yichi Zhang & Man Li & Ziyi Liang & Chao Ding, 2021. "Who Will Establish New Trade Relations? Looking for Potential Relationship in International Nickel Trade," Sustainability, MDPI, vol. 13(21), pages 1-15, October.
    8. Nora Connor & Albert Barberán & Aaron Clauset, 2017. "Using null models to infer microbial co-occurrence networks," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-23, May.
    9. Aslan, Serpil & Kaya, Buket & Kaya, Mehmet, 2019. "Predicting potential links by using strengthened projections in evolving bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 998-1011.
    10. Leto Peel & Tiago P. Peixoto & Manlio De Domenico, 2022. "Statistical inference links data and theory in network science," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    11. Rafiee, Samira & Salavati, Chiman & Abdollahpouri, Alireza, 2020. "CNDP: Link prediction based on common neighbors degree penalization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    12. Bikramjit Das & Tiandong Wang & Gengling Dai, 2022. "Asymptotic Behavior of Common Connections in Sparse Random Networks," Methodology and Computing in Applied Probability, Springer, vol. 24(3), pages 2071-2092, September.
    13. Xiaowen Xi & Jiaqi Wei & Ying Guo & Weiyu Duan, 2022. "Academic collaborations: a recommender framework spanning research interests and network topology," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6787-6808, November.
    14. Lei Wang & Shuo Yu & Falih Gozi Febrinanto & Fayez Alqahtani & Tarek E. El-Tobely, 2022. "Fairness-Aware Predictive Graph Learning in Social Networks," Mathematics, MDPI, vol. 10(15), pages 1-19, July.
    15. Lee, Yan-Li & Zhou, Tao, 2021. "Collaborative filtering approach to link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    16. Greg Morrison & L Mahadevan, 2012. "Discovering Communities through Friendship," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-9, July.
    17. Liu, Zhenfeng & Feng, Jian & Uden, Lorna, 2023. "Technology opportunity analysis using hierarchical semantic networks and dual link prediction," Technovation, Elsevier, vol. 128(C).
    18. Shugang Li & Ziming Wang & Beiyan Zhang & Boyi Zhu & Zhifang Wen & Zhaoxu Yu, 2022. "The Research of “Products Rapidly Attracting Users” Based on the Fully Integrated Link Prediction Algorithm," Mathematics, MDPI, vol. 10(14), pages 1-19, July.
    19. Chunjiang Liu & Yikun Han & Haiyun Xu & Shihan Yang & Kaidi Wang & Yongye Su, 2024. "A Community Detection and Graph-Neural-Network-Based Link Prediction Approach for Scientific Literature," Mathematics, MDPI, vol. 12(3), pages 1-20, January.
    20. Chengjun Zhang & Jin Liu & Yanzhen Qu & Tianqi Han & Xujun Ge & An Zeng, 2018. "Enhancing the robustness of recommender systems against spammers," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-14, November.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pcbi00:1002574. 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: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    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.