Predicting miRNA-disease associations via layer attention graph convolutional network model
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1472-6947
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
online resource.
- Bibliographic citation
-
Predicting miRNA-disease associations via layer attention graph convolutional network model ; volume:22 ; number:1 ; day:19 ; month:3 ; year:2022 ; pages:1-8 ; date:12.2022
BMC medical informatics and decision making ; 22, Heft 1 (19.3.2022), 1-8, 12.2022
- Creator
-
Han, Han
Zhu, Rong
Liu, Jin-Xing
Dai, Ling-Yun
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1186/s12911-022-01807-8
- URN
-
urn:nbn:de:101:1-2022061108523109061527
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
- 15.08.2025, 7:29 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Han, Han
- Zhu, Rong
- Liu, Jin-Xing
- Dai, Ling-Yun
- SpringerLink (Online service)
Other Objects (12)
Predicting anticancer hyperfoods with graph convolutional networks
MAGCNSE: predicting lncRNA-disease associations using multi-view attention graph convolutional network and stacking ensemble model
Dual graph convolutional neural network for predicting chemical networks
Predicting functions of maize proteins using graph convolutional network
Predicting Propositional Satisfiability Based on Graph Attention Networks
TARGCN: temporal attention recurrent graph convolutional neural network for traffic prediction
Graph convolutional and attention models for entity classification in multilayer networks
Attention mechanism-enhanced graph convolutional neural network for unbalanced lithology identification
BIKAGCN: Knowledge-Aware Recommendations Under Bi-layer Graph Convolutional Networks
A Novel Attention‐Based Layer Pruning Approach for Low‐Complexity Convolutional Neural Networks
KAGN:knowledge-powered attention and graph convolutional networks for social media rumor detection
A heterogeneous graph convolutional attention network method for classification of autism spectrum disorder
Predicting anticancer hyperfoods with graph convolutional networks
MAGCNSE: predicting lncRNA-disease associations using multi-view attention graph convolutional network and stacking ensemble model
Dual graph convolutional neural network for predicting chemical networks
Predicting functions of maize proteins using graph convolutional network
Predicting Propositional Satisfiability Based on Graph Attention Networks
TARGCN: temporal attention recurrent graph convolutional neural network for traffic prediction
Graph convolutional and attention models for entity classification in multilayer networks
Attention mechanism-enhanced graph convolutional neural network for unbalanced lithology identification
BIKAGCN: Knowledge-Aware Recommendations Under Bi-layer Graph Convolutional Networks
A Novel Attention‐Based Layer Pruning Approach for Low‐Complexity Convolutional Neural Networks
KAGN:knowledge-powered attention and graph convolutional networks for social media rumor detection
A heterogeneous graph convolutional attention network method for classification of autism spectrum disorder
Predicting anticancer hyperfoods with graph convolutional networks
MAGCNSE: predicting lncRNA-disease associations using multi-view attention graph convolutional network and stacking ensemble model
Dual graph convolutional neural network for predicting chemical networks
Predicting functions of maize proteins using graph convolutional network
Predicting Propositional Satisfiability Based on Graph Attention Networks
TARGCN: temporal attention recurrent graph convolutional neural network for traffic prediction
Graph convolutional and attention models for entity classification in multilayer networks
Attention mechanism-enhanced graph convolutional neural network for unbalanced lithology identification
BIKAGCN: Knowledge-Aware Recommendations Under Bi-layer Graph Convolutional Networks
A Novel Attention‐Based Layer Pruning Approach for Low‐Complexity Convolutional Neural Networks
KAGN:knowledge-powered attention and graph convolutional networks for social media rumor detection