MSGCA: Drug-Disease Associations Prediction Based on Multi-Similarities Graph Convolutional Autoencoder

Identifying drug-disease associations (DDAs) is critical to the development of drugs. Traditional methods to determine DDAs are expensive and inefficient. Therefore, it is imperative to develop more accurate and effective methods for DDAs prediction. Most current DDAs prediction methods utilize orig...

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Vydáno v:IEEE journal of biomedical and health informatics Ročník 27; číslo 7; s. 3686 - 3694
Hlavní autoři: Wang, Ying, Gao, Ying-Lian, Wang, Juan, Li, Feng, Liu, Jin-Xing
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.07.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Convolutional neural networks
Diseases
Drug development
Drug-disease associations
Drugs
graph convolutional autoencoder
Heterogeneous networks
Humans
Kernel
Kernels
linear neighborhood
Machine learning
Mathematical analysis
multi-similarities
Prediction algorithms
Predictions
Reproducibility of Results
Similarity
Sparse matrices
ISSN:2168-2194, 2168-2208, 2168-2208
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Shrnutí:Identifying drug-disease associations (DDAs) is critical to the development of drugs. Traditional methods to determine DDAs are expensive and inefficient. Therefore, it is imperative to develop more accurate and effective methods for DDAs prediction. Most current DDAs prediction methods utilize original DDAs matrix directly. However, the original DDAs matrix is sparse, which greatly affects the prediction consequences. Hence, a prediction method based on multi-similarities graph convolutional autoencoder (MSGCA) is proposed for DDAs prediction. First, MSGCA integrates multiple drug similarities and disease similarities using centered kernel alignment-based multiple kernel learning (CKA-MKL) algorithm to form new drug similarity and disease similarity, respectively. Second, the new drug and disease similarities are improved by linear neighborhood, and the DDAs matrix is reconstructed by weighted K nearest neighbor profiles. Next, the reconstructed DDAs and the improved drug and disease similarities are integrated into a heterogeneous network. Finally, the graph convolutional autoencoder with attention mechanism is utilized to predict DDAs. Compared with extant methods, MSGCA shows superior results on three datasets. Furthermore, case studies further demonstrate the reliability of MSGCA.
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ISSN:2168-2194
2168-2208
2168-2208
DOI:10.1109/JBHI.2023.3272154