Cargando…

LaGAT: link-aware graph attention network for drug–drug interaction prediction

MOTIVATION: Drug–drug interaction (DDI) prediction is a challenging problem in pharmacology and clinical applications. With the increasing availability of large biomedical databases, large-scale biological knowledge graphs containing drug information have been widely used for DDI prediction. However...

Descripción completa

Detalles Bibliográficos
Autores principales: Hong, Yue, Luo, Pengyu, Jin, Shuting, Liu, Xiangrong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9750103/
https://www.ncbi.nlm.nih.gov/pubmed/36271850
http://dx.doi.org/10.1093/bioinformatics/btac682
_version_ 1784850178924085248
author Hong, Yue
Luo, Pengyu
Jin, Shuting
Liu, Xiangrong
author_facet Hong, Yue
Luo, Pengyu
Jin, Shuting
Liu, Xiangrong
author_sort Hong, Yue
collection PubMed
description MOTIVATION: Drug–drug interaction (DDI) prediction is a challenging problem in pharmacology and clinical applications. With the increasing availability of large biomedical databases, large-scale biological knowledge graphs containing drug information have been widely used for DDI prediction. However, large knowledge graphs inevitably suffer from data noise problems, which limit the performance and interpretability of models based on the knowledge graph. Recent studies attempt to improve models by introducing inductive bias through an attention mechanism. However, they all only depend on the topology of entity nodes independently to generate fixed attention pathways, without considering the semantic diversity of entity nodes in different drug pair links. This makes it difficult for models to select more meaningful nodes to overcome data quality limitations and make more interpretable predictions. RESULTS: To address this issue, we propose a Link-aware Graph Attention method for DDI prediction, called LaGAT, which is able to generate different attention pathways for drug entities based on different drug pair links. For a drug pair link, the LaGAT uses the embedding representation of one of the drugs as a query vector to calculate the attention weights, thereby selecting the appropriate topological neighbor nodes to obtain the semantic information of the other drug. We separately conduct experiments on binary and multi-class classification and visualize the attention pathways generated by the model. The results prove that LaGAT can better capture semantic relationships and achieves remarkably superior performance over both the classical and state-of-the-art models on DDI prediction. AVAILABILITYAND IMPLEMENTATION: The source code and data are available at https://github.com/Azra3lzz/LaGAT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
format Online
Article
Text
id pubmed-9750103
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-97501032022-12-15 LaGAT: link-aware graph attention network for drug–drug interaction prediction Hong, Yue Luo, Pengyu Jin, Shuting Liu, Xiangrong Bioinformatics Original Paper MOTIVATION: Drug–drug interaction (DDI) prediction is a challenging problem in pharmacology and clinical applications. With the increasing availability of large biomedical databases, large-scale biological knowledge graphs containing drug information have been widely used for DDI prediction. However, large knowledge graphs inevitably suffer from data noise problems, which limit the performance and interpretability of models based on the knowledge graph. Recent studies attempt to improve models by introducing inductive bias through an attention mechanism. However, they all only depend on the topology of entity nodes independently to generate fixed attention pathways, without considering the semantic diversity of entity nodes in different drug pair links. This makes it difficult for models to select more meaningful nodes to overcome data quality limitations and make more interpretable predictions. RESULTS: To address this issue, we propose a Link-aware Graph Attention method for DDI prediction, called LaGAT, which is able to generate different attention pathways for drug entities based on different drug pair links. For a drug pair link, the LaGAT uses the embedding representation of one of the drugs as a query vector to calculate the attention weights, thereby selecting the appropriate topological neighbor nodes to obtain the semantic information of the other drug. We separately conduct experiments on binary and multi-class classification and visualize the attention pathways generated by the model. The results prove that LaGAT can better capture semantic relationships and achieves remarkably superior performance over both the classical and state-of-the-art models on DDI prediction. AVAILABILITYAND IMPLEMENTATION: The source code and data are available at https://github.com/Azra3lzz/LaGAT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2022-10-22 /pmc/articles/PMC9750103/ /pubmed/36271850 http://dx.doi.org/10.1093/bioinformatics/btac682 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Paper
Hong, Yue
Luo, Pengyu
Jin, Shuting
Liu, Xiangrong
LaGAT: link-aware graph attention network for drug–drug interaction prediction
title LaGAT: link-aware graph attention network for drug–drug interaction prediction
title_full LaGAT: link-aware graph attention network for drug–drug interaction prediction
title_fullStr LaGAT: link-aware graph attention network for drug–drug interaction prediction
title_full_unstemmed LaGAT: link-aware graph attention network for drug–drug interaction prediction
title_short LaGAT: link-aware graph attention network for drug–drug interaction prediction
title_sort lagat: link-aware graph attention network for drug–drug interaction prediction
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9750103/
https://www.ncbi.nlm.nih.gov/pubmed/36271850
http://dx.doi.org/10.1093/bioinformatics/btac682
work_keys_str_mv AT hongyue lagatlinkawaregraphattentionnetworkfordrugdruginteractionprediction
AT luopengyu lagatlinkawaregraphattentionnetworkfordrugdruginteractionprediction
AT jinshuting lagatlinkawaregraphattentionnetworkfordrugdruginteractionprediction
AT liuxiangrong lagatlinkawaregraphattentionnetworkfordrugdruginteractionprediction