ReviewAnnual review of pharmacology and toxicology2017
Harnessing Big Data for Systems Pharmacology.
Review in Annual review of pharmacology and toxicology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Computational Drug Discovery, Development, and Repurposing for Ebola Virus Disease Treatment.Molecules (Basel, Switzerland) · 2017Pooled it
- Computational pharmacology-based molecular mechanism investigation of cinnamaldehyde intervention in nephrotic syndrome.Naunyn-Schmiedeberg's archives of pharmacology · 2025Article
- Exploring Scoring Function Space: Developing Computational Models for Drug Discovery.Current medicinal chemistry · 2024Review
- Hierarchical multi-omics data integration and modeling predict cell-specific chemical proteomics and drug responses.Cell reports methods · 2023Article
- Development of and insights from systems pharmacology models of antibody-drug conjugates.CPT: pharmacometrics & systems pharmacology · 2022Review
- Exploring the Mechanism of Icariin in Osteoporosis Based on a Network Pharmacology Strategy.Medical science monitor : international medical journal of experimental and clinical research · 2020Article
- Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling.Drug discovery today · 2020Review
- Drug Research Meets Network Science: Where Are We?Journal of medicinal chemistry · 2020Article
- Big Data and Artificial Intelligence Modeling for Drug Discovery.Annual review of pharmacology and toxicology · 2020Review
- A Systems Pharmacology Approach for Identifying the Multiple Mechanisms of Action for the Rougui-Fuzi Herb Pair in the Treatment of Cardiocerebral Vascular Diseases.Evidence-based complementary and alternative medicine : eCAM · 2020Article
- Applications of Quantitative Systems Pharmacology in Model-Informed Drug Discovery: Perspective on Impact and Opportunities.CPT: pharmacometrics & systems pharmacology · 2019Article
- Systems pharmacology approach reveals protective mechanisms of Jian-Pi Qing-Chang decoction on ulcerative colitis.World journal of gastroenterology · 2019Article
- Article
- Structural Insights into Characterizing Binding Sites in Epidermal Growth Factor Receptor Kinase Mutants.Journal of chemical information and modeling · 2019Article
- Heterogeneous Multi-Layered Network Model for Omics Data Integration and Analysis.Frontiers in genetics · 2019Review
- Network Pharmacology Analysis ofEvidence-based complementary and alternative medicine : eCAM · 2019Article
- Systematic analyses of drugs and disease indications in RepurposeDB reveal pharmacological, biological and epidemiological factors influencing drug repositioning.Briefings in bioinformatics · 2018Article
- Whither systems medicine?Experimental & molecular medicine · 2018Review
- Automating drug discovery.Nature reviews. Drug discovery · 2018Review
- Data Portal for the Library of Integrated Network-based Cellular Signatures (LINCS) program: integrated access to diverse large-scale cellular perturbation response data.Nucleic acids research · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.