Prediction of miRNA-disease associations in microbes based on graph convolutional networks and autoencoders Article Swipe
Qingquan Liao
,
Yuxiang Ye
,
Zihang Li
,
Hao Chen
,
Linlin Zhuo
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fmicb.2023.1170559
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fmicb.2023.1170559
MicroRNAs (miRNAs) are short RNA molecular fragments that regulate gene expression by targeting and inhibiting the expression of specific RNAs. Due to the fact that microRNAs affect many diseases in microbial ecology, it is necessary to predict microRNAs' association with diseases at the microbial level. To this end, we propose a novel model, termed as GCNA-MDA, where dual-autoencoder and graph convolutional network (GCN) are integrated to predict miRNA-disease association. The proposed method leverages autoencoders to extract robust representations of miRNAs and diseases and meantime exploits GCN to capture the topological information of miRNA-disease networks. To alleviate the impact of insufficient information for the original data, the association similarity and feature similarity data are combined to calculate a more complete initial basic vector of nodes. The experimental results on the benchmark datasets demonstrate that compared with the existing representative methods, the proposed method has achieved the superior performance and its precision reaches up to 0.8982. These results demonstrate that the proposed method can serve as a tool for exploring miRNA-disease associations in microbial environments.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fmicb.2023.1170559
- https://www.frontiersin.org/articles/10.3389/fmicb.2023.1170559/pdf
- OA Status
- gold
- Cited By
- 15
- References
- 56
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4367317875
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4367317875Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fmicb.2023.1170559Digital Object Identifier
- Title
-
Prediction of miRNA-disease associations in microbes based on graph convolutional networks and autoencodersWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-04-28Full publication date if available
- Authors
-
Qingquan Liao, Yuxiang Ye, Zihang Li, Hao Chen, Linlin ZhuoList of authors in order
- Landing page
-
https://doi.org/10.3389/fmicb.2023.1170559Publisher landing page
- PDF URL
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https://www.frontiersin.org/articles/10.3389/fmicb.2023.1170559/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.frontiersin.org/articles/10.3389/fmicb.2023.1170559/pdfDirect OA link when available
- Concepts
-
Autoencoder, microRNA, Computational biology, Computer science, Graph, Similarity (geometry), Artificial intelligence, Benchmark (surveying), Support vector machine, Data mining, Biology, Deep learning, Machine learning, Gene, Theoretical computer science, Genetics, Geography, Image (mathematics), GeodesyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
15Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 8, 2023: 3Per-year citation counts (last 5 years)
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56Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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