In:
PLOS ONE, Public Library of Science (PLoS), Vol. 17, No. 8 ( 2022-8-29), p. e0273764-
Abstract:
Drug–drug interaction (DDI) prediction has received considerable attention from industry and academia. Most existing methods predict DDIs from drug attributes or relationships with neighbors, which does not guarantee that informative drug embeddings for prediction will be obtained. To address this limitation, we propose a multitype drug interaction prediction method based on the deep fusion of drug features and topological relationships, abbreviated DM-DDI. The proposed method adopts a deep fusion strategy to combine drug features and topologies to learn representative drug embeddings for DDI prediction. Specifically, a deep neural network model is first used on the drug feature matrix to extract feature information, while a graph convolutional network model is employed to capture structural information from the adjacency matrix. Then, we adopt delivery operations that allow the two models to exchange information between layers, as well as an attention mechanism for a weighted fusion of the two learned embeddings before the output layer. Finally, the unified drug embeddings for the downstream task are obtained. We conducted extensive experiments on real-world datasets, the experimental results demonstrated that DM-DDI achieved more accurate prediction results than state-of-the-art baselines. Furthermore, in two tasks that are more similar to real-world scenarios, DM-DDI outperformed other prediction methods for unknown drugs.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0273764
DOI:
10.1371/journal.pone.0273764.g001
DOI:
10.1371/journal.pone.0273764.g002
DOI:
10.1371/journal.pone.0273764.g003
DOI:
10.1371/journal.pone.0273764.g004
DOI:
10.1371/journal.pone.0273764.g005
DOI:
10.1371/journal.pone.0273764.g006
DOI:
10.1371/journal.pone.0273764.g007
DOI:
10.1371/journal.pone.0273764.t001
DOI:
10.1371/journal.pone.0273764.t002
DOI:
10.1371/journal.pone.0273764.t003
DOI:
10.1371/journal.pone.0273764.t004
DOI:
10.1371/journal.pone.0273764.t005
DOI:
10.1371/journal.pone.0273764.t006
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2267670-3
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