MSGCA: Drug-Disease Associations Prediction Based on Multi-Similarities Graph Convolutional Autoencoder
Author:
Affiliation:
1. School of Computer Science, Qufu Normal University, Rizhao, China
2. Qufu Normal University Library, Qufu Normal University, Rizhao, China
Funder
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics
Link
http://xplorestaging.ieee.org/ielx7/6221020/10171108/10122563.pdf?arnumber=10122563
Reference52 articles.
1. Identification of Drug-Disease Associations Using Information of Molecular Structures and Clinical Symptoms via Deep Convolutional Neural Network
2. HINGRL: predicting drug–disease associations with graph representation learning on heterogeneous information networks
3. deepDR: a network-based deep learning approach to in silico drug repositioning
4. Convolutional Neural Network and Bidirectional Long Short-Term Memory-Based Method for Predicting Drug–Disease Associations
5. MGRCDA: Metagraph Recommendation Method for Predicting CircRNA-Disease Association
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2. KFDAE: CircRNA-Disease Associations Prediction Based on Kernel Fusion and Deep Auto-Encoder;IEEE Journal of Biomedical and Health Informatics;2024-05
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4. PU‐GNN: A Positive‐Unlabeled Learning Method for Polypharmacy Side‐Effects Detection Based on Graph Neural Networks;International Journal of Intelligent Systems;2024-01
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