Harnessing Big Data for Systems Pharmacology

被引:34
作者
Xie, Lei [1 ,2 ]
Draizen, Eli J. [3 ,4 ]
Bourne, Philip E. [3 ,5 ]
机构
[1] CUNY, Hunter Coll, Dept Comp Sci, New York, NY 10065 USA
[2] CUNY, Grad Ctr, New York, NY 10016 USA
[3] NIH, Natl Ctr Biotechnol Informat, Natl Lib Med, Bethesda, MD 20894 USA
[4] Boston Univ, Program Bioinformat, Boston, MA 02215 USA
[5] NIH, Off Director, Bethesda, MD 20894 USA
来源
ANNUAL REVIEW OF PHARMACOLOGY AND TOXICOLOGY, VOL 57 | 2017年 / 57卷
关键词
cloud computing; data science; machine learning; semantic web; computational modeling; systems biology; systems pharmacology modeling; NIH Commons; SEMANTIC WEB TECHNOLOGIES; NEED EXACT SOLVERS; DRUG; MODELS; GENOMICS; DISEASE; BIOLOGY; KNOWLEDGEBASE; MECHANISMS; RESOURCES;
D O I
10.1146/annurev-pharmtox-010716-104659
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
Systems pharmacology aims to holistically understand mechanisms of drug actions to support drug discovery and clinical practice. Systems pharmacology modeling (SPM) is data driven. It integrates an exponentially growing amount of data at multiple scales (genetic, molecular, cellular, organismal, and environmental). The goal of SPM is to develop mechanistic or predictive multiscale models that are interpretable and actionable. The current explosions in genomics and other omics data, as well as the tremendous advances in big data technologies, have already enabled biologists to generate novel hypotheses and gain new knowledge through computational models of genome-wide, heterogeneous, and dynamic data sets. More work is needed to interpret and predict a drug response phenotype, which is dependent on many known and unknown factors. To gain a comprehensive understanding of drug actions, SPM requires close collaborations between domain experts from diverse fields and integration of heterogeneous models from biophysics, mathematics, statistics, machine learning, and semantic webs. This creates challenges in model management, model integration, model translation, and knowledge integration. In this review, we discuss several emergent issues in SPM and potential solutions using big data technology and analytics. The concurrent development of high-throughput techniques, cloud computing, data science, and the semantic web will likely allow SPM to be findable, accessible, interoperable, reusable, reliable, interpretable, and actionable.
引用
收藏
页码:245 / 262
页数:18
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