Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/140003
Title: Ensemble prediction of synergistic drug combinations incorporating biological, chemical, pharmacological, and network knowledge
Authors: Ding, Pingjian
Yin, Rui
Luo, Jiawei
Kwoh, Chee-Keong
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Ding, P., Yin, R., Luo, J., & Kwoh, C.-K. (2019). Ensemble prediction of synergistic drug combinations incorporating biological, chemical, pharmacological, and network knowledge. IEEE Journal of Biomedical and Health Informatics, 23(3), 1336-1345. doi:10.1109/JBHI.2018.2852274
Journal: IEEE Journal of Biomedical and Health Informatics 
Abstract: Combinatorial therapy may reduce drug side effects and improve drug efficacy, making combination therapy a promising strategy to treat complex diseases. However, in the existing computational methods, the natural properties and network knowledge of drugs have not been adequately and simultaneously considered, making it difficult to identify effective drug combinations. Computational methods that incorporate multiple sources of information (biological, chemical, pharmacological, and network knowledge) offer more opportunities to screen synergistic drug combinations. Therefore, we developed a novel Ensemble Prediction framework of Synergistic Drug Combinations (EPSDC) to accurately and efficiently predict drug combinations by integrating information from multiple-sources. EPSDC constructs feature vector of drug pair by concatenating different types of drug similarities, and then uses these groups in a feature-based base predictor. Next, transductive learning is applied on heterogeneous drug-target networks to achieve a network-based score for the drug pair. Finally, two types of ensemble rules are introduced to combine the feature-based score and the network-based score, and then potential drug combinations are prioritized. To demonstrate the effect of the ensemble rule, comprehensive experiments were conducted to compare single models and ensemble models. The experimental results indicated that our method outperformed the state-of-the-art method in five-fold cross validation and de novo prediction tests on the two benchmark datasets. We further analyzed the effect of maximum length of the meta-path and the impacts of different types of features. Moreover, the practical usefulness of our method was confirmed in the predicted novel drug combinations. The source code of EPSDC is available at https://github.com/KDDing/EPSDC.
URI: https://hdl.handle.net/10356/140003
ISSN: 2168-2194
DOI: 10.1109/JBHI.2018.2852274
Rights: © 2018 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

SCOPUSTM   
Citations

4
checked on Sep 5, 2020

WEB OF SCIENCETM
Citations 50

6
checked on Oct 22, 2020

Page view(s) 50

12
checked on Oct 26, 2020

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.