GCFMCL: predicting miRNA-drug sensitivity using graph collaborative filtering and multi-view contrastive learning Article Swipe
Jinhang Wei
,
Linlin Zhuo
,
Zhecheng Zhou
,
Xinze Lian
,
Xiangzheng Fu
,
Xiaojun Yao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/bib/bbad247
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/bib/bbad247
Studies have shown that the mechanism of action of many drugs is related to miRNA. In-depth research on the relationship between miRNA and drugs can provide theoretical foundations and practical approaches for various areas, such as drug target discovery, drug repositioning and biomarker research. Traditional biological experiments to test miRNA-drug susceptibility are costly and time-consuming. Thus, sequence- or topology-based deep learning methods are recognized in this field for their efficiency and accuracy. However, these methods have limitations in dealing with sparse topologies and higher-order information of miRNA (drug) feature. In this work, we propose GCFMCL, a model for multi-view contrastive learning based on graph collaborative filtering. To the best of our knowledge, this is the first attempt that incorporates contrastive learning strategy into the graph collaborative filtering framework to predict the sensitivity relationships between miRNA and drug. The proposed multi-view contrastive learning method is divided into topological contrastive objective and feature contrastive objective: (1) For the homogeneous neighbors of the topological graph, we propose a novel topological contrastive learning method via constructing the contrastive target through the topological neighborhood information of nodes. (2) The proposed model obtains feature contrastive targets from high-order feature information according to the correlation of node features, and mines potential neighborhood relationships in the feature space. The proposed multi-view comparative learning effectively alleviates the impact of heterogeneous node noise and graph data sparsity in graph collaborative filtering, and significantly enhances the performance of the model. Our study employs a dataset derived from the NoncoRNA and ncDR databases, encompassing 2049 experimentally validated miRNA-drug sensitivity associations. Five-fold cross-validation shows that the Area Under the Curve (AUC), Area Under the Precision-Recall Curve (AUPR) and F1-score (F1) of GCFMCL reach 95.28%, 95.66% and 89.77%, which outperforms the state-of-the-art (SOTA) method by the margin of 2.73%, 3.42% and 4.96%, respectively. Our code and data can be accessed at https://github.com/kkkayle/GCFMCL.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/bib/bbad247
- https://academic.oup.com/bib/article-pdf/24/4/bbad247/50917390/bbad247.pdf
- OA Status
- bronze
- Cited By
- 29
- References
- 56
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4383711298
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4383711298Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1093/bib/bbad247Digital Object Identifier
- Title
-
GCFMCL: predicting miRNA-drug sensitivity using graph collaborative filtering and multi-view contrastive learningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-07-01Full publication date if available
- Authors
-
Jinhang Wei, Linlin Zhuo, Zhecheng Zhou, Xinze Lian, Xiangzheng Fu, Xiaojun YaoList of authors in order
- Landing page
-
https://doi.org/10.1093/bib/bbad247Publisher landing page
- PDF URL
-
https://academic.oup.com/bib/article-pdf/24/4/bbad247/50917390/bbad247.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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bronzeOpen access status per OpenAlex
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https://academic.oup.com/bib/article-pdf/24/4/bbad247/50917390/bbad247.pdfDirect OA link when available
- Concepts
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Computer science, Graph, Artificial intelligence, Feature (linguistics), Machine learning, Feature learning, Node (physics), Drug target, Theoretical computer science, Pharmacology, Medicine, Engineering, Linguistics, Philosophy, Structural engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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29Total citation count in OpenAlex
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2025: 10, 2024: 13, 2023: 6Per-year citation counts (last 5 years)
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56Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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