Biomolecular Network-Based Synergistic Drug Combination Discovery
Joint Authors
Xie, Lu
Qin, Guangrong
Li, Xiangyi
Yang, Qingmin
Chen, Lanming
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-11-07
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Drug combination is a powerful and promising approach for complex disease therapy such as cancer and cardiovascular disease.
However, the number of synergistic drug combinations approved by the Food and Drug Administration is very small.
To bridge the gap between urgent need and low yield, researchers have constructed various models to identify synergistic drug combinations.
Among these models, biomolecular network-based model is outstanding because of its ability to reflect and illustrate the relationships among drugs, disease-related genes, therapeutic targets, and disease-specific signaling pathways as a system.
In this review, we analyzed and classified models for synergistic drug combination prediction in recent decade according to their respective algorithms.
Besides, we collected useful resources including databases and analysis tools for synergistic drug combination prediction.
It should provide a quick resource for computational biologists who work with network medicine or synergistic drug combination designing.
American Psychological Association (APA)
Li, Xiangyi& Qin, Guangrong& Yang, Qingmin& Chen, Lanming& Xie, Lu. 2016. Biomolecular Network-Based Synergistic Drug Combination Discovery. BioMed Research International،Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1099078
Modern Language Association (MLA)
Li, Xiangyi…[et al.]. Biomolecular Network-Based Synergistic Drug Combination Discovery. BioMed Research International No. 2016 (2016), pp.1-11.
https://search.emarefa.net/detail/BIM-1099078
American Medical Association (AMA)
Li, Xiangyi& Qin, Guangrong& Yang, Qingmin& Chen, Lanming& Xie, Lu. Biomolecular Network-Based Synergistic Drug Combination Discovery. BioMed Research International. 2016. Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1099078
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099078