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Predicting Drug-drug Interactions Using Heterogeneous Graph Attention Networks

Authors:

Khaled Mohammed Saifuddin,

Muhammad Ifte Khairul Islam,

Esra AkbasAuthors Info & Claims

BCB '23: Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

Article No.: 48, Pages 1 - 6

https://doi.org/10.1145/3584371.3613065

Published: 04 October 2023 Publication History

Abstract

Drug-Drug Interactions (DDIs) can alter a drug's efficacy and lead to adverse effects. Predicting potential DDIs during clinical trials is challenging; thus, computational methods are gaining prominence. We present a novel DDI prediction method, constructing a Heterogeneous Information Network (HIN) integrating biomedical entities such as drugs, proteins, and side effects. Our end-to-end model, HAN-DDI, based on a heterogeneous graph attention network, demonstrates superior accuracy in predicting DDIs, surpassing existing methods.

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Cited By

Dara OIbrahim AMohammed T(2024)Advancing medical imaging: detecting polypharmacy and adverse drug effects with Graph Convolutional Networks (GCN)BMC Medical Imaging10.1186/s12880-024-01349-724:1Online publication date: 15-Jul-2024
https://doi.org/10.1186/s12880-024-01349-7
Tanvir FSaifuddin KIslam MAkbas E(2024)DDI Prediction With Heterogeneous Information Network - Meta-Path Based ApproachIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2024.341771521:5(1168-1179)Online publication date: Sep-2024
https://doi.org/10.1109/TCBB.2024.3417715
Huang SWang MZheng XChen JTang C(2024)Hierarchical and Dynamic Graph Attention Network for Drug-Disease Association PredictionIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2024.336308028:4(2416-2427)Online publication date: Apr-2024
https://doi.org/10.1109/JBHI.2024.3363080

Index Terms

Predicting Drug-drug Interactions Using Heterogeneous Graph Attention Networks
1. Information systems
  1. Information systems applications
    1. Computing platforms
    2. Data mining

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cover image ACM Conferences

BCB '23: Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

September 2023

626 pages

ISBN:9798400701269

DOI:10.1145/3584371

General Chairs:
May D. Wang
SGeorgia Institute of Technology
,
Byung-Jun Yoon

Copyright © 2023 ACM.

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New York, NY, United States

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Published: 04 October 2023

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BCB '23: 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

September 3 - 6, 2023

TX, Houston, USA

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Cited By

Dara OIbrahim AMohammed T(2024)Advancing medical imaging: detecting polypharmacy and adverse drug effects with Graph Convolutional Networks (GCN)BMC Medical Imaging10.1186/s12880-024-01349-724:1Online publication date: 15-Jul-2024
https://doi.org/10.1186/s12880-024-01349-7
Tanvir FSaifuddin KIslam MAkbas E(2024)DDI Prediction With Heterogeneous Information Network - Meta-Path Based ApproachIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2024.341771521:5(1168-1179)Online publication date: Sep-2024
https://doi.org/10.1109/TCBB.2024.3417715
Huang SWang MZheng XChen JTang C(2024)Hierarchical and Dynamic Graph Attention Network for Drug-Disease Association PredictionIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2024.336308028:4(2416-2427)Online publication date: Apr-2024
https://doi.org/10.1109/JBHI.2024.3363080

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