ArticleIET systems biology2019
Drug repositioning via matrix completion with multi-view side information.
Article in IET systems biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- Strategies and efforts in circumventing the emergence of antiviral resistance against conventional antivirals.npj antimicrobials and resistance · 2025Review
- Efficient prediction of drug-drug interaction using deep learning models.IET systems biology · 2020Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the process of drug discovery and disease treatment, drug repositioning is broadly studied to identify biological targets for existing drugs. Many methods have been proposed for drug-target interaction prediction by taking into account different kinds of data sources. However, most of the existing methods only use one side information for drugs or targets to predict new targets for drugs. Some recent works have improved the prediction accuracy by jointly considering multiple representations of drugs and targets. In this work, the authors propose a drug-target prediction approach by matrix completion with multi-view side information (MCM) of drugs and proteins from both structural view and chemical view. Different from existing studies for drug-target prediction, they predict drug-target interaction by directly completing the interaction matrix between them. The experimental results show that the MCM method could obtain significantly higher accuracies than the comparison methods. They finally report new drug-target interactions for 26 FDA-approved drugs, and biologically discuss these targets using existing references.
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Registered trials
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