Knowledge Mining of Interactions between Drugs from the Extensive Literature with a Novel Graph-Convolutional-Network-Based Method

Xu, X; Meng, F; Sun, L

Knowledge Mining of Interactions between Drugs from the Extensive Literature with a Novel Graph-Convolutional-Network-Based Method

Xu, X Meng, F Sun, L

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: ELECTRONICS, 2023, 12, (2)
Issue Date:: 2023-01

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Field	Value	Language
dc.contributor.author	Xu, X
dc.contributor.author	Meng, F
dc.contributor.author	Sun, L https://orcid.org/0000-0002-9830-1527
dc.date.accessioned	2024-03-28T04:04:41Z
dc.date.available	2024-03-28T04:04:41Z
dc.date.issued	2023-01
dc.identifier.citation	ELECTRONICS, 2023, 12, (2)
dc.identifier.issn	1450-5843
dc.identifier.issn	2079-9292
dc.identifier.uri	http://hdl.handle.net/10453/177338
dc.description.abstract	<jats:p>Interactions between drugs can occur when two or more drugs are used for the same patient. This may result in changes in the drug’s pharmacological activity, some of which are beneficial and some of which are harmful. Thus, identifying possible drug–drug interactions (DDIs) has always been a crucial research topic in the field of clinical pharmacology. As clinical trials are time-consuming and expensive, current approaches for predicting DDIs are mainly based on knowledge mining from the literature using computational methods. However, since the literature contain a large amount of unrelated information, the task of identifying drug interactions with high confidence has become challenging. Thus, here, we present a novel graph-convolutional-network-based method called DDINN to detect potential DDIs. Combining cBiLSTM, graph convolutional networks and weight-rebalanced dependency matrix, DDINN is able to extract both contexture and syntactic information efficiently from the extensive biomedical literature. At last, we compare our DDINN with some other state-of-the-art models, and it is proved that our work is more effective. In addition, the ablation experiments demonstrate the advantages of DDINN’s optimization techniques as well.</jats:p>
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	ELECTRONICS
dc.relation.isbasedon	10.3390/electronics12020311
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.title	Knowledge Mining of Interactions between Drugs from the Extensive Literature with a Novel Graph-Convolutional-Network-Based Method
dc.type	Journal Article
utslib.citation.volume	12
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2024-03-28T04:04:39Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	12
utslib.citation.issue	2

Abstract:

Interactions between drugs can occur when two or more drugs are used for the same patient. This may result in changes in the drug’s pharmacological activity, some of which are beneficial and some of which are harmful. Thus, identifying possible drug–drug interactions (DDIs) has always been a crucial research topic in the field of clinical pharmacology. As clinical trials are time-consuming and expensive, current approaches for predicting DDIs are mainly based on knowledge mining from the literature using computational methods. However, since the literature contain a large amount of unrelated information, the task of identifying drug interactions with high confidence has become challenging. Thus, here, we present a novel graph-convolutional-network-based method called DDINN to detect potential DDIs. Combining cBiLSTM, graph convolutional networks and weight-rebalanced dependency matrix, DDINN is able to extract both contexture and syntactic information efficiently from the extensive biomedical literature. At last, we compare our DDINN with some other state-of-the-art models, and it is proved that our work is more effective. In addition, the ablation experiments demonstrate the advantages of DDINN’s optimization techniques as well.

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http://hdl.handle.net/10453/177338