A Review of Computational Drug Repositioning Approaches

被引:5
|
作者
Huang, Guohua [1 ,2 ,3 ]
Li, Jincheng [1 ,2 ]
Wang, Peng [3 ]
Li, Weibiao [3 ]
机构
[1] Shaoyang Univ, Prov Key Lab Informat Serv Rural Area Southwester, Shaoyang 422000, Hunan, Peoples R China
[2] Shaoyang Univ, Coll Informat Engn, Shaoyang 422000, Hunan, Peoples R China
[3] Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410081, Hunan, Peoples R China
基金
湖南省自然科学基金; 中国国家自然科学基金;
关键词
Drug repositioning; heterogeneous network; machine learning; gene expression profile; phenotype; drug target; TARGET INTERACTION NETWORKS; CONNECTIVITY MAP; EXPRESSION SIGNATURES; PREDICTION; IDENTIFICATION; DISCOVERY; ASSOCIATION; INFORMATION; INTEGRATION; CANDIDATES;
D O I
10.2174/1386207321666171221112835
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Aims & Scope: Computational drug repositioning emerges as a new idea of drug discovery and development. Contrary to conventional routines, computational drug repositioning encompasses low risk and high safety. Some successful cases demonstrated its advantage. Therefore, a large number of computational drug repositioning approaches have been developed over the past decades. We summarized briefly these methods and classified them into target-based, gene-expression-based, phenome-based and multi-omics-based categories according to strategies of drug repositioning. Conclusion: We reviewed some representatives of computational drug repositioning methods in each category, with emphasis on detail of techniques and finally discussed developing trends of computational drug repositioning.
引用
收藏
页码:831 / 838
页数:8
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