BiRNN-DDI: A Drug-Drug Interaction Event Type Prediction Model Based on Bidirectional Recurrent Neural Network and Graph2Seq Representation-Reference-Cited by-同舟云学术

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BiRNN-DDI: A Drug-Drug Interaction Event Type Prediction Model Based on Bidirectional Recurrent Neural Network and Graph2Seq Representation

Published:2024-07-25 Issue: Volume: Page:
ISSN:1557-8666
Container-title:Journal of Computational Biology
language:en
Short-container-title:Journal of Computational Biology

Author:

Wang GuiShen¹^ORCID,Feng Hui¹,Cao Chen²

Affiliation:

1. School of Computer Science and Engineering, Changchun University of Technology, Changchun, China.

2. Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.

Publisher

Mary Ann Liebert Inc

Link

https://www.liebertpub.com/doi/pdf/10.1089/cmb.2024.0476

Reference46 articles.

1. Prediction of drug-drug interaction events using graph neural networks based feature extraction

2. Machine learning approaches and databases for prediction of drug–target interaction: a survey paper

3. Modular Multi–Source Prediction of Drug Side–Effects With DruGNN

4. Potential Adverse Drug Events and Drug–Drug Interactions with Medical and Consumer Cannabidiol (CBD) Use

5. Disentangling genetic feature selection and aggregation in transcriptome-wide association studies

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