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- W2290999697 abstract "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." @default.
- W2290999697 created "2016-06-24" @default.
- W2290999697 creator A5066245750 @default.
- W2290999697 creator A5068604431 @default.
- W2290999697 creator A5075833022 @default.
- W2290999697 date "2017-01-06" @default.
- W2290999697 modified "2023-10-17" @default.
- W2290999697 title "Harnessing Big Data for Systems Pharmacology" @default.
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- W2290999697 doi "https://doi.org/10.1146/annurev-pharmtox-010716-104659" @default.
- W2290999697 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5626567" @default.
- W2290999697 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/27814027" @default.
- W2290999697 hasPublicationYear "2017" @default.
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