Predicting associations among drugs, targets and diseases by tensor decomposition for drug repositioning
Open accessAbstract
Background: Development of new drugs is a time-consuming and costly process, and the cost is still increasing in recent years. However, the number of drugs approved by FDA every year per dollar spent on development is declining. Drug repositioning, which aims to find new use of existing drugs, attracts attention of pharmaceutical researchers due to its high efficiency. A variety of computational methods for drug repositioning have been proposed based on machine learning approaches, network-based approaches, matrix decomposition approaches, etc. Results: We propose a novel computational method for drug repositioning. We construct and decompose three-dimensional tensors, which consist of the associations among drugs, targets and diseases, to derive latent factors reflecting the functional patterns of the three kinds of entities. The proposed method outperforms several baseline methods in recovering missing associations. Most of the top predictions are validated by literature search and computational docking. Latent factors are used to cluster the drugs, targets and diseases into functional groups. Topological Data Analysis (TDA) is applied to investigate the properties of the clusters. We find that the latent factors are able to capture the functional patterns and underlying molecular mechanisms of drugs, targets and diseases. In addition, we focus on repurposing drugs for cancer and discover not only new therapeutic use but also adverse effects of the drugs. In the in-depth study of associations among the clusters of drugs, targets and cancer subtypes, we find there exist strong associations between particular clusters. Conclusions: The proposed method is able to recover missing associations, discover new predictions and uncover functional clusters of drugs, targets and diseases. The clustering of drugs, targets and diseases, as well as the associations among the clusters, provides a new guiding framework for drug repositioning.
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From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [166]
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From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [166]
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- Authors
- Wang, R., Li, S., Cheng, L., Wong, M. H., & Leung, K. S.
- Affiliations
- Chinese University of Hong Kong (HK)
- Jinan University (CN)
- Corresponding author
- Wang, Ran
- Venue
- BMC Bioinformatics
- Published
- 2019-12
- Volume
- 20
- Issue
- S26
- Pages
- 628-628
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC6912989
- OpenAlex
- W2995659175
- Cited by
- 37
- References
- 54
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 12 · 2023: 6 · 2022: 9 · 2021: 5 · 2020: 2
- FWCI
- 2.72
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Chinese University of Hong Kong
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- drug repurposing, tensor decomposition, latent variables, topological data analysis, drug clustering, functional clusters, adverse drug reactions, tumor subtypes
- MeSH terms
- Machine Learning, Humans, Cluster Analysis, Computational Biology, Drug Repositioning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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