Systems Pharmacology: A Unified Framework for Prediction of Drug-Target Interactions

被引:19
作者
Duc-Hau Le [1 ]
Ly Le [2 ]
机构
[1] Water Resources Univ, Sch Comp Sci & Engn, 175 Tay Son, Hanoi, Vietnam
[2] Vietnam Natl Univ, Int Univ, Sch Biotechnol, Ho Chi Minh, Vietnam
关键词
Drug-target interaction; network-based approach; machine learning-based approach; drug-disease association; disease-gene association; drug-gene-disease association; INTERACTION NETWORKS; RANDOM-WALK; PROTEIN INTERACTIONS; GENE PRIORITIZATION; DATA FUSION; IDENTIFICATION; KERNELS; BIOLOGY; DISCOVERY; TOOLS;
D O I
10.2174/1381612822666160418121534
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
Background: Drug discovery is one important issue in medicine and pharmacology area. Traditional methods using target-based approach are usually time-consuming and ineffective. Recently, the problems are approached in a system-level view and therefore it is called systems pharmacology. This research field deals with the problems in drug discovery by integrating various kinds of biomedical and pharmacological data and using advanced computational methods. Ultimately, the problems are more effectively solved. One of the most important problem in systems pharmacology is prediction of drug-target interactions. Methods: In this review, we are going to summarize various computational methods for this problem. Results: More importantly, we formed a unified framework for the problem. In addition, to study human health and disease in a more systematically and effectively, we also presented an integrated scheme for a wider problem of prediction of disease-gene-drug associations. Conclusion: By presenting the unified framework and the integrated scheme, underlying computational methods for problems in systems pharmacology can be understood and complex relationships among diseases, genes and drugs can be identified effectively.
引用
收藏
页码:3569 / 3575
页数:7
相关论文
共 108 条
[1]   NETWORKING FOR NEW DRUGS [J].
Ainsworth, Claire .
NATURE MEDICINE, 2011, 17 (10) :1166-1168
[2]   DT-Web: a web-based application for drug-target interaction and drug combination prediction through domain-tuned network-based inference [J].
Alaimo, Salvatore ;
Bonnici, Vincenzo ;
Cancemi, Damiano ;
Ferro, Alfredo ;
Giugno, Rosalba ;
Pulvirenti, Alfredo .
BMC SYSTEMS BIOLOGY, 2015, 9
[3]   Literature mining, ontologies and information visualization for drug repurposing [J].
Andronis, Christos ;
Sharma, Anuj ;
Virvilis, Vassilis ;
Deftereos, Spyros ;
Persidis, Aris .
BRIEFINGS IN BIOINFORMATICS, 2011, 12 (04) :357-368
[4]  
[Anonymous], 2015, P 6 INT S INF COMM T
[5]   Drug repositioning: Identifying and developing new uses for existing drugs [J].
Ashburn, TT ;
Thor, KB .
NATURE REVIEWS DRUG DISCOVERY, 2004, 3 (08) :673-683
[6]   Network medicine: a network-based approach to human disease [J].
Barabasi, Albert-Laszlo ;
Gulbahce, Natali ;
Loscalzo, Joseph .
NATURE REVIEWS GENETICS, 2011, 12 (01) :56-68
[7]   Network analyses in systems pharmacology [J].
Berger, Seth I. ;
Iyengar, Ravi .
BIOINFORMATICS, 2009, 25 (19) :2466-2472
[8]   Supervised prediction of drug-target interactions using bipartite local models [J].
Bleakley, Kevin ;
Yamanishi, Yoshihiro .
BIOINFORMATICS, 2009, 25 (18) :2397-2403
[9]   Systems Biology and Systems Chemistry: New Directions for Drug Discovery [J].
Brown, J. B. ;
Okuno, Yasushi .
CHEMISTRY & BIOLOGY, 2012, 19 (01) :23-28
[10]   Drug target identification using side-effect similarity [J].
Campillos, Monica ;
Kuhn, Michael ;
Gavin, Anne-Claude ;
Jensen, Lars Juhl ;
Bork, Peer .
SCIENCE, 2008, 321 (5886) :263-266