A review of network-based approaches to drug repositioning.
Experimental drug development is time-consuming, expensive and limited to a relatively small number of targets. However, recent studies show that repositioning of existing drugs can function more efficiently than de novo experimental drug development to minimize costs and risks. Previous studies have proven that network analysis is a versatile platform for this purpose, as the biological networks are used to model interactions between many different biological concepts. The present study is an attempt to review network-based methods in predicting drug targets for drug repositioning. For each method, the preferred type of data set is described, and their advantages and limitations are discussed. For each method, we seek to provide a brief description, as well as an evaluation based on its performance metrics.We conclude that integrating distinct and complementary data should be used because each type of data set reveals a unique aspect of information about an organism. We also suggest that applying a standard set of evaluation metrics and data sets would be essential in this fast-growing research domain.
Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran.
Maryam
M
Lotfi Shahreza
Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran.
Nasser
N
Ghadiri
Imperial College London.
Sayed Rasoul
SR
Mousavi
Drug Delivery Systems Research Center of Isfahan University of Medical Sciences.
Jaleh
J
Varshosaz
Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran.
James R
JR
Green
D019295:Computational Biology / Q000379:methods; D062313:Databases, Pharmaceutical / Q000706:statistics & numerical data; D004347:Drug Interactions; D058492:Drug Repositioning / Q000145:classification / Q000379:methods* / Q000706:statistics & numerical data; D064420:Drug-Related Side Effects and Adverse Reactions; D053263:Gene Regulatory Networks; D006801:Humans; D000069550:Machine Learning; D053858:Metabolic Networks and Pathways; D062105:Molecular Docking Simulation / Q000706:statistics & numerical data; D060066:Protein Interaction Maps