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A novel framework for the identification of drug target proteins: Combining stacked auto-encoders with a biased support vector machine

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  • Qi Wang
  • YangHe Feng
  • JinCai Huang
  • TengJiao Wang
  • GuangQuan Cheng

Abstract

The identification of drug target proteins (IDTP) plays a critical role in biometrics. The aim of this study was to retrieve potential drug target proteins (DTPs) from a collected protein dataset, which represents an overwhelming task of great significance. Previously reported methodologies for this task generally employ protein-protein interactive networks but neglect informative biochemical attributes. We formulated a novel framework utilizing biochemical attributes to address this problem. In the framework, a biased support vector machine (BSVM) was combined with the deep embedded representation extracted using a deep learning model, stacked auto-encoders (SAEs). In cases of non-drug target proteins (NDTPs) contaminated by DTPs, the framework is beneficial due to the efficient representation of the SAE and relief of the imbalance effect by the BSVM. The experimental results demonstrated the effectiveness of our framework, and the generalization capability was confirmed via comparisons to other models. This study is the first to exploit a deep learning model for IDTP. In summary, nearly 23% of the NDTPs were predicted as likely DTPs, which are awaiting further verification based on biomedical experiments.

Suggested Citation

  • Qi Wang & YangHe Feng & JinCai Huang & TengJiao Wang & GuangQuan Cheng, 2017. "A novel framework for the identification of drug target proteins: Combining stacked auto-encoders with a biased support vector machine," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-18, April.
  • Handle: RePEc:plo:pone00:0176486
    DOI: 10.1371/journal.pone.0176486
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    References listed on IDEAS

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    1. 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.
    2. Hua Yu & Jianxin Chen & Xue Xu & Yan Li & Huihui Zhao & Yupeng Fang & Xiuxiu Li & Wei Zhou & Wei Wang & Yonghua Wang, 2012. "A Systematic Prediction of Multiple Drug-Target Interactions from Chemical, Genomic, and Pharmacological Data," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-14, May.
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