MAGCNSE: predicting lncRNA-disease associations using multi-view attention graph convolutional network and stacking ensemble model Article Swipe
Ying Liang
,
Zequn Zhang
,
Nian-Nian Liu
,
Yanan Wu
,
Changlong Gu
,
Yinglong Wang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1186/s12859-022-04715-w
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1186/s12859-022-04715-w
Background Many long non-coding RNAs (lncRNAs) have key roles in different human biologic processes and are closely linked to numerous human diseases, according to cumulative evidence. Predicting potential lncRNA-disease associations can help to detect disease biomarkers and perform disease analysis and prevention. Establishing effective computational methods for lncRNA-disease association prediction is critical. Results In this paper, we propose a novel model named MAGCNSE to predict underlying lncRNA-disease associations. We first obtain multiple feature matrices from the multi-view similarity graphs of lncRNAs and diseases utilizing graph convolutional network. Then, the weights are adaptively assigned to different feature matrices of lncRNAs and diseases using the attention mechanism. Next, the final representations of lncRNAs and diseases is acquired by further extracting features from the multi-channel feature matrices of lncRNAs and diseases using convolutional neural network. Finally, we employ a stacking ensemble classifier, consisting of multiple traditional machine learning classifiers, to make the final prediction. The results of ablation studies in both representation learning methods and classification methods demonstrate the validity of each module. Furthermore, we compare the overall performance of MAGCNSE with that of six other state-of-the-art models, the results show that it outperforms the other methods. Moreover, we verify the effectiveness of using multi-view data of lncRNAs and diseases. Case studies further reveal the outstanding ability of MAGCNSE in the identification of potential lncRNA-disease associations. Conclusions The experimental results indicate that MAGCNSE is a useful approach for predicting potential lncRNA-disease associations.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s12859-022-04715-w
- https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-022-04715-w
- OA Status
- gold
- Cited By
- 40
- References
- 68
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4280587098
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4280587098Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1186/s12859-022-04715-wDigital Object Identifier
- Title
-
MAGCNSE: predicting lncRNA-disease associations using multi-view attention graph convolutional network and stacking ensemble modelWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-05-19Full publication date if available
- Authors
-
Ying Liang, Zequn Zhang, Nian-Nian Liu, Yanan Wu, Changlong Gu, Yinglong WangList of authors in order
- Landing page
-
https://doi.org/10.1186/s12859-022-04715-wPublisher landing page
- PDF URL
-
https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-022-04715-wDirect 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://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-022-04715-wDirect OA link when available
- Concepts
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Computer science, Convolutional neural network, Machine learning, Artificial intelligence, Classifier (UML), Graph, Ensemble learning, Feature learning, Disease, Pattern recognition (psychology), Data mining, Theoretical computer science, Medicine, PathologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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40Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7, 2024: 17, 2023: 12, 2022: 4Per-year citation counts (last 5 years)
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68Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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