ArticleCPT: pharmacometrics & systems pharmacology2015
Integrating Multiple Evidence Sources to Predict Adverse Drug Reactions Based on a Systems Pharmacology Model.
Article in CPT: pharmacometrics & systems pharmacology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Artificial Intelligence Models and Tools for the Assessment of Drug-Herb Interactions.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Graph reasoning method enhanced by relational transformers and knowledge distillation for drug-related side effect prediction.iScience · 2024Article
- Interpretable Drug-to-Drug Network Features for Predicting Adverse Drug Reactions.Healthcare (Basel, Switzerland) · 2023Article
- Prediction of drug side effects with transductive matrix co-completion.Bioinformatics (Oxford, England) · 2023Article
- Prediction of adverse drug reactions based on knowledge graph embedding.BMC medical informatics and decision making · 2021Article
- A compound attributes-based predictive model for drug induced liver injury in humans.PloS one · 2020Article
- DDIGIP: predicting drug-drug interactions based on Gaussian interaction profile kernels.BMC bioinformatics · 2019Article
- Learning important features from multi-view data to predict drug side effects.Journal of cheminformatics · 2019Article
- Predicting adverse drug reactions through interpretable deep learning framework.BMC bioinformatics · 2018Article
- PyBioMed: a python library for various molecular representations of chemicals, proteins and DNAs and their interactions.Journal of cheminformatics · 2018Article
- Expanding biological space coverage enhances the prediction of drug adverse effects in human using in vitro activity profiles.Scientific reports · 2018Article
- ChemSAR: an online pipelining platform for molecular SAR modeling.Journal of cheminformatics · 2017Article
- Examination and Estimation of Anticholinergic Burden: Current Trends and Implications for Future Research.Drugs & aging · 2016Article
- TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models.Journal of computer-aided molecular design · 2016Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying potential adverse drug reactions (ADRs) is critically important for drug discovery and public health. Here we developed a multiple evidence fusion (MEF) method for the large-scale prediction of drug ADRs that can handle both approved drugs and novel molecules. MEF is based on the similarity reference by collaborative filtering, and integrates multiple similarity measures from various data types, taking advantage of the complementarity in the data. We used MEF to integrate drug-related and ADR-related data from multiple levels, including the network structural data formed by known drug-ADR relationships for predicting likely unknown ADRs. On cross-validation, it obtains high sensitivity and specificity, substantially outperforming existing methods that utilize single or a few data types. We validated our prediction by their overlap with drug-ADR associations that are known in databases. The proposed computational method could be used for complementary hypothesis generation and rapid analysis of potential drug-ADR interactions.
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Registered trials
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