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Chu Y, Kaushik AC, Wang X, et al. DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features. Brief Bioinform. 2021;22(1):451-462doi: 10.1093/bib/bbz152.
Chu, Y., Kaushik, A. C., Wang, X., Wang, W., Zhang, Y., Shan, X., Salahub, D. R., Xiong, Y., & Wei, D. Q. (2021). DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features. Briefings in bioinformatics, 22(1), 451-462. https://doi.org/10.1093/bib/bbz152
Chu, Yanyi, et al. "DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features." Briefings in bioinformatics vol. 22,1 (2021): 451-462. doi: https://doi.org/10.1093/bib/bbz152
Chu Y, Kaushik AC, Wang X, Wang W, Zhang Y, Shan X, Salahub DR, Xiong Y, Wei DQ. DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features. Brief Bioinform. 2021 Jan 18;22(1):451-462. doi: 10.1093/bib/bbz152. PMID: 31885041.
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Brief Bioinform. 2021 Jan 18;22(1):451-462. doi: 10.1093/bib/bbz152.

DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features.

Briefings in bioinformatics

Yanyi Chu, Aman Chandra Kaushik, Xiangeng Wang, Wei Wang, Yufang Zhang, Xiaoqi Shan, Dennis Russell Salahub, Yi Xiong, Dong-Qing Wei

Affiliations

  1. School of Life Sciences and Biotechnology, Shanghai Jiao Tong University.
  2. School of Medicine, Jiangnan University, Wuxi, China.
  3. Mathematical Sciences, Shanghai Jiao Tong University.
  4. School of Life Sciences and Biotechnology.
  5. Department of Chemistry, University of Calgary, Fellow Royal Society of Canada.

PMID: 31885041 DOI: 10.1093/bib/bbz152

Abstract

Drug-target interactions (DTIs) play a crucial role in target-based drug discovery and development. Computational prediction of DTIs can effectively complement experimental wet-lab techniques for the identification of DTIs, which are typically time- and resource-consuming. However, the performances of the current DTI prediction approaches suffer from a problem of low precision and high false-positive rate. In this study, we aim to develop a novel DTI prediction method for improving the prediction performance based on a cascade deep forest (CDF) model, named DTI-CDF, with multiple similarity-based features between drugs and the similarity-based features between target proteins extracted from the heterogeneous graph, which contains known DTIs. In the experiments, we built five replicates of 10-fold cross-validation under three different experimental settings of data sets, namely, corresponding DTI values of certain drugs (SD), targets (ST), or drug-target pairs (SP) in the training sets are missed but existed in the test sets. The experimental results demonstrate that our proposed approach DTI-CDF achieves a significantly higher performance than that of the traditional ensemble learning-based methods such as random forest and XGBoost, deep neural network, and the state-of-the-art methods such as DDR. Furthermore, there are 1352 newly predicted DTIs which are proved to be correct by KEGG and DrugBank databases. The data sets and source code are freely available at https://github.com//a96123155/DTI-CDF.

© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected].

Keywords: cascade deep forest; drug-target interaction; ensemble learning; machine learning

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