Ensemble Learning Prediction of Drug-Target Interactions Using GIST Descriptor Extracted from PSSM-Based Evolutionary Information
Joint Authors
You, Zhu-Hong
Zhan, Xinke
Yu, Changqing
Li, Liping
Pan, Jie
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-24
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Identifying the drug-target interactions (DTIs) plays an essential role in new drug development.
However, there still has the limited knowledge of DTIs and a significant number of unknown DTI pairs.
Moreover, the traditional experimental methods have inevitable disadvantages such as high cost and time-consuming.
Therefore, developing computational methods for predicting DTIs is attracting more and more attention.
In this study, we report a novel computational approach for predicting DTI using GIST feature, position-specific scoring matrix (PSSM), and rotation forest (RF).
Specifically, each target protein is first converted into a PSSM for retaining evolutionary information.
Then, the GIST feature is extracted from PSSM and substructure fingerprint information is adopted to extract the feature of the drug.
Finally, combining each protein and drug features to form a new drug-target pair, which is employed as input feature for RF classifier.
In the experiment, the proposed method achieves high average accuracies of 89.25%, 85.93%, 82.36%, and 73.89% on enzyme, ion channel, G protein-coupled receptors (GPCRs), and nuclear receptor, respectively.
For further evaluating the prediction performance of the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on the same golden standard dataset.
These promising results illustrate that the proposed method is more effective and stable than other methods.
We expect the proposed method to be a useful tool for predicting large-scale DTIs.
American Psychological Association (APA)
Zhan, Xinke& You, Zhu-Hong& Yu, Changqing& Li, Liping& Pan, Jie. 2020. Ensemble Learning Prediction of Drug-Target Interactions Using GIST Descriptor Extracted from PSSM-Based Evolutionary Information. BioMed Research International،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1133960
Modern Language Association (MLA)
Zhan, Xinke…[et al.]. Ensemble Learning Prediction of Drug-Target Interactions Using GIST Descriptor Extracted from PSSM-Based Evolutionary Information. BioMed Research International No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1133960
American Medical Association (AMA)
Zhan, Xinke& You, Zhu-Hong& Yu, Changqing& Li, Liping& Pan, Jie. Ensemble Learning Prediction of Drug-Target Interactions Using GIST Descriptor Extracted from PSSM-Based Evolutionary Information. BioMed Research International. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1133960
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1133960