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iDrug: Integration of drug repositioning and drug-target prediction via cross-network embedding
- Source :
- PLoS Computational Biology, Vol 16, Iss 7, p e1008040 (2020), PLoS Computational Biology
- Publication Year :
- 2020
- Publisher :
- Public Library of Science (PLoS), 2020.
-
Abstract
- Computational drug repositioning and drug-target prediction have become essential tasks in the early stage of drug discovery. In previous studies, these two tasks have often been considered separately. However, the entities studied in these two tasks (i.e., drugs, targets, and diseases) are inherently related. On one hand, drugs interact with targets in cells to modulate target activities, which in turn alter biological pathways to promote healthy functions and to treat diseases. On the other hand, both drug repositioning and drug-target prediction involve the same drug feature space, which naturally connects these two problems and the two domains (diseases and targets). By using the wisdom of the crowds, it is possible to transfer knowledge from one of the domains to the other. The existence of relationships among drug-target-disease motivates us to jointly consider drug repositioning and drug-target prediction in drug discovery. In this paper, we present a novel approach called iDrug, which seamlessly integrates drug repositioning and drug-target prediction into one coherent model via cross-network embedding. In particular, we provide a principled way to transfer knowledge from these two domains and to enhance prediction performance for both tasks. Using real-world datasets, we demonstrate that iDrug achieves superior performance on both learning tasks compared to several state-of-the-art approaches. Our code and datasets are available at: https://github.com/Case-esaC/iDrug.<br />Author summary Traditional high-throughput techniques for drug discovery are often expensive, time-consuming, and with high failure rates. Computational drug repositioning and drug-target prediction have thus become essential tasks in the early stage drug discovery. The emergence of large-scale heterogeneous biological networks has offered unprecedented opportunities for developing machine learning approaches to identify novel drug-disease or drug-target interactions. However, most existing works focused either on the drug-disease network or on the drug-target network, thus failed to capture the inherent dependencies between these two networks. These two biological networks are naturally connected since they involve the same drug feature space. In our opinion, ignoring this rich source of information is a major shortcoming of some existing works. In this paper, we present a novel approach called iDrug, which seamlessly integrates the drug-disease network and the drug-target network into one coherent model via cross-network embedding. As a result, iDrug is able to take full usage of the knowledge within these two biological networks to better exploit new biomedical insights of drug-target-disease. Therefore, iDrug has broad applications in drug discovery.
- Subjects :
- 0301 basic medicine
Databases, Factual
Computer science
Drug target
Network embedding
computer.software_genre
Machine Learning
0302 clinical medicine
Crowds
Drug Discovery
Medicine and Health Sciences
Drug Interactions
Biology (General)
Databases, Protein
Ecology
Drug discovery
Pharmaceutics
Applied Mathematics
Simulation and Modeling
Drug Information
Drug repositioning
Polytherapy Drug Treatment
Computational Theory and Mathematics
Modeling and Simulation
Area Under Curve
Physical Sciences
Embedding
Algorithms
Research Article
Optimization
Computer and Information Sciences
Drug Research and Development
QH301-705.5
Feature vector
Drug-Drug Interactions
Machine learning
Research and Analysis Methods
03 medical and health sciences
Cellular and Molecular Neuroscience
Machine Learning Algorithms
Drug Therapy
Drug Development
Protein Domains
Artificial Intelligence
Genetics
Code (cryptography)
Humans
Computer Simulation
Molecular Biology
Ecology, Evolution, Behavior and Systematics
Pharmacology
Models, Statistical
business.industry
Drug Repositioning
Computational Biology
MicroRNAs
030104 developmental biology
ROC Curve
Artificial intelligence
business
computer
030217 neurology & neurosurgery
Mathematics
Software
Subjects
Details
- Language :
- English
- ISSN :
- 15537358
- Volume :
- 16
- Issue :
- 7
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....611ebca307130ce93a954349ef94e34f