ArticleScientific reports2017
CODA: Integrating multi-level context-oriented directed associations for analysis of drug effects.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 17 papers.
What it found
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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
17 citing papers in PubMed, 29 citations in OpenAlex.
- Trial
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- Drug repurposing for Parkinson's disease by biological pathway based edge-weighted network proximity analysis.Scientific reports · 2024Article
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- Diverse and Synergistic Actions of Phytochemicals in a Plant-Based Multivitamin/Mineral Supplement against Oxidative Stress and Inflammation in Healthy Individuals: A Systems Biology Approach Based on a Randomized Clinical Trial.Antioxidants (Basel, Switzerland) · 2023Article
- In silico activity and ADMET profiling of phytochemicals from Ethiopian indigenous aloes using pharmacophore models.Scientific reports · 2022Article
- A machine learning model for classifying G-protein-coupled receptors as agonists or antagonists.BMC bioinformatics · 2022Article
- Literature mining for context-specific molecular relations using multimodal representations (COMMODAR).BMC bioinformatics · 2020Review
- A Systems Biological Approach to Understanding the Mechanisms Underlying the Therapeutic Potential of Red Ginseng Supplements against Metabolic Diseases.Molecules (Basel, Switzerland) · 2020Article
- Synergistic mechanisms of Sanghuang-Danshen phytochemicals on postprandial vascular dysfunction in healthy subjects: A network biology approach based on a clinical trial.Scientific reports · 2019Article
- Meta-path Based Prioritization of Functional Drug Actions with Multi-Level Biological Networks.Scientific reports · 2019Article
- Developments in toxicogenomics: understanding and predicting compound-induced toxicity from gene expression data.Molecular omics · 2018Review
- Phenotype-oriented network analysis for discovering pharmacological effects of natural compounds.Scientific reports · 2018Article
- A systematic approach to identify therapeutic effects of natural products based on human metabolite information.BMC bioinformatics · 2018Article
- Petri net-based prediction of therapeutic targets that recover abnormally phosphorylated proteins in muscle atrophy.BMC systems biology · 2018Article
Corrections and comments
- Erratum issued
- Erratum issued
Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In silico network-based methods have shown promising results in the field of drug development. Yet, most of networks used in the previous research have not included context information even though biological associations actually do appear in the specific contexts. Here, we reconstruct an anatomical context-specific network by assigning contexts to biological associations using protein expression data and scientific literature. Furthermore, we employ the context-specific network for the analysis of drug effects with a proximity measure between drug targets and diseases. Distinct from previous context-specific networks, intercellular associations and phenomic level entities such as biological processes are included in our network to represent the human body. It is observed that performances in inferring drug-disease associations are increased by adding context information and phenomic level entities. In particular, hypertension, a disease related to multiple organs and associated with several phenomic level entities, is analyzed in detail to investigate how our network facilitates the inference of drug-disease associations. Our results indicate that the inclusion of context information, intercellular associations, and phenomic level entities can contribute towards a better prediction of drug-disease associations and provide detailed insight into understanding of how drugs affect diseases in the human body.
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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.