IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0008126.html
   My bibliography  Save this article

Prediction of Pharmacological and Xenobiotic Responses to Drugs Based on Time Course Gene Expression Profiles

Author

Listed:
  • Tao Huang
  • WeiRen Cui
  • LeLe Hu
  • KaiYan Feng
  • Yi-Xue Li
  • Yu-Dong Cai

Abstract

More and more people are concerned by the risk of unexpected side effects observed in the later steps of the development of new drugs, either in late clinical development or after marketing approval. In order to reduce the risk of the side effects, it is important to look out for the possible xenobiotic responses at an early stage. We attempt such an effort through a prediction by assuming that similarities in microarray profiles indicate shared mechanisms of action and/or toxicological responses among the chemicals being compared. A large time course microarray database derived from livers of compound-treated rats with thirty-four distinct pharmacological and toxicological responses were studied. The mRMR (Minimum-Redundancy-Maximum-Relevance) method and IFS (Incremental Feature Selection) were used to select a compact feature set (141 features) for the reduction of feature dimension and improvement of prediction performance. With these 141 features, the Leave-one-out cross-validation prediction accuracy of first order response using NNA (Nearest Neighbor Algorithm) was 63.9%. Our method can be used for pharmacological and xenobiotic responses prediction of new compounds and accelerate drug development.

Suggested Citation

  • Tao Huang & WeiRen Cui & LeLe Hu & KaiYan Feng & Yi-Xue Li & Yu-Dong Cai, 2009. "Prediction of Pharmacological and Xenobiotic Responses to Drugs Based on Time Course Gene Expression Profiles," PLOS ONE, Public Library of Science, vol. 4(12), pages 1-7, December.
  • Handle: RePEc:plo:pone00:0008126
    DOI: 10.1371/journal.pone.0008126
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0008126
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0008126&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0008126?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
    ---><---

    Citations

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


    Cited by:

    1. Jianhua Guan & Zuguo Yu & Yongan Liao & Runbin Tang & Ming Duan & Guosheng Han, 2024. "Predicting Critical Path of Labor Dispute Resolution in Legal Domain by Machine Learning Models Based on SHapley Additive exPlanations and Soft Voting Strategy," Mathematics, MDPI, vol. 12(2), pages 1-17, January.
    2. Yu-Dong Cai & Tao Huang & Kai-Yan Feng & Lele Hu & Lu Xie, 2010. "A Unified 35-Gene Signature for both Subtype Classification and Survival Prediction in Diffuse Large B-Cell Lymphomas," PLOS ONE, Public Library of Science, vol. 5(9), pages 1-8, September.
    3. Bi-Qing Li & Tao Huang & Jian Zhang & Ning Zhang & Guo-Hua Huang & Lei Liu & Yu-Dong Cai, 2013. "An Ensemble Prognostic Model for Colorectal Cancer," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-8, May.
    4. Lei Chen & Chen Chu & Xiangyin Kong & Guohua Huang & Tao Huang & Yu-Dong Cai, 2015. "A Hybrid Computational Method for the Discovery of Novel Reproduction-Related Genes," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-15, March.
    5. Le-Le Hu & Shen Niu & Tao Huang & Kai Wang & Xiao-He Shi & Yu-Dong Cai, 2010. "Prediction and Analysis of Protein Hydroxyproline and Hydroxylysine," PLOS ONE, Public Library of Science, vol. 5(12), pages 1-8, December.
    6. Lu-Lu Zheng & Shen Niu & Pei Hao & KaiYan Feng & Yu-Dong Cai & Yixue Li, 2011. "Prediction of Protein Modification Sites of Pyrrolidone Carboxylic Acid Using mRMR Feature Selection and Analysis," PLOS ONE, Public Library of Science, vol. 6(12), pages 1-11, December.
    7. Tao Huang & Lei Chen & Yu-Dong Cai & Kuo-Chen Chou, 2011. "Classification and Analysis of Regulatory Pathways Using Graph Property, Biochemical and Physicochemical Property, and Functional Property," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-11, September.
    8. Tao Huang & Ping Wang & Zhi-Qiang Ye & Heng Xu & Zhisong He & Kai-Yan Feng & LeLe Hu & WeiRen Cui & Kai Wang & Xiao Dong & Lu Xie & Xiangyin Kong & Yu-Dong Cai & Yixue Li, 2010. "Prediction of Deleterious Non-Synonymous SNPs Based on Protein Interaction Network and Hybrid Properties," PLOS ONE, Public Library of Science, vol. 5(7), pages 1-7, July.

    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:pone00:0008126. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.