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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A biomedical knowledge graph-based method for drug–drug interactions prediction through combining local and global features with deep neural networks
Zhong-Hao Ren, Zhu‐Hong You, Chang-Qing Yu, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 5
Closed Access | Times Cited: 43

Showing 1-25 of 43 citing articles:

Application of Artificial Intelligence in Drug–Drug Interactions Prediction: A Review
Yuanyuan Zhang, Zengqian Deng, Xiaoyu Xu, et al.
Journal of Chemical Information and Modeling (2023) Vol. 64, Iss. 7, pp. 2158-2173
Closed Access | Times Cited: 33
An effective framework for predicting drug–drug interactions based on molecular substructures and knowledge graph neural network
Siqi Chen, Ivan Semenov, Fengyun Zhang, et al.
Computers in Biology and Medicine (2023) Vol. 169, pp. 107900-107900
Closed Access | Times Cited: 32
Emerging drug interaction prediction enabled by a flow-based graph neural network with biomedical network
Yongqi Zhang, Quanming Yao, Ling Yue, et al.
Nature Computational Science (2023) Vol. 3, Iss. 12, pp. 1023-1033
Closed Access | Times Cited: 24
Drug-drug interactions prediction based on deep learning and knowledge graph: A review
Huimin Luo, Weijie Yin, Jianlin Wang, et al.
iScience (2024) Vol. 27, Iss. 3, pp. 109148-109148
Open Access | Times Cited: 14
Deep learning for drug‐drug interaction prediction: A comprehensive review
Xinyue Li, Zhankun Xiong, Wen Zhang, et al.
Quantitative Biology (2024) Vol. 12, Iss. 1, pp. 30-52
Open Access | Times Cited: 8
3D graph neural network with few-shot learning for predicting drug–drug interactions in scaffold-based cold start scenario
Qiujie Lv, Jun Zhou, Ziduo Yang, et al.
Neural Networks (2023) Vol. 165, pp. 94-105
Closed Access | Times Cited: 20
BCMCMI: A Fusion Model for Predicting circRNA-miRNA Interactions Combining Semantic and Meta-path
Meng-Meng Wei, Chang-Qing Yu, Liping Li, et al.
Journal of Chemical Information and Modeling (2023) Vol. 63, Iss. 16, pp. 5384-5394
Closed Access | Times Cited: 18
Application of machine learning in drug side effect prediction: databases, methods, and challenges
Haochen Zhao, Jian Zhong, Xiao Liang, et al.
Frontiers of Computer Science (2024) Vol. 19, Iss. 5
Open Access | Times Cited: 7
Relation-aware graph structure embedding with co-contrastive learning for drug–drug interaction prediction
Mengying Jiang, Guizhong Liu, Biao Zhao, et al.
Neurocomputing (2024) Vol. 572, pp. 127203-127203
Open Access | Times Cited: 5
BEROLECMI: a novel prediction method to infer circRNA-miRNA interaction from the role definition of molecular attributes and biological networks
Xin-Fei Wang, Chang-Qing Yu, Zhu‐Hong You, et al.
BMC Bioinformatics (2024) Vol. 25, Iss. 1
Open Access | Times Cited: 5
HetDDI: a pre-trained heterogeneous graph neural network model for drug–drug interaction prediction
Zhe Li, Xinyi Tu, Yuping Chen, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 6
Open Access | Times Cited: 11
SubGE-DDI: A new prediction model for drug-drug interaction established through biomedical texts and drug-pairs knowledge subgraph enhancement
Yiyang Shi, Mingxiu He, Junheng Chen, et al.
PLoS Computational Biology (2024) Vol. 20, Iss. 4, pp. e1011989-e1011989
Open Access | Times Cited: 4
Using graph neural network to conduct supplier recommendation based on large-scale supply chain
Yuchun Tu, Wenxin Li, Xiao Song, et al.
International Journal of Production Research (2024) Vol. 62, Iss. 24, pp. 8595-8608
Closed Access | Times Cited: 4
Beyond scaleup: Knowledge‐aware parsimony learning from deep networks
Quanming Yao, Yongqi Zhang, Yaqing Wang, et al.
AI Magazine (2025) Vol. 46, Iss. 1
Open Access
MOLGAECL: Molecular Graph Contrastive Learning via Graph Auto-Encoder Pretraining and Fine-Tuning Based on Drug–Drug Interaction Prediction
Yu Li, Lin-Xuan Hou, Hai-Cheng Yi, et al.
Journal of Chemical Information and Modeling (2025)
Closed Access
A systematic review of molecular structures, knowledge graphs, and cold-start scenario in drug-drug interaction prediction
Majed Ahmad, Zamarud Shah, Hui Wen Nies
Computers in Biology and Medicine (2025) Vol. 190, pp. 110122-110122
Closed Access
Prediction of circRNA–miRNA Interaction Using Graph Attention Network Based on Molecular Attributes and Biological Networks
Abdullah Almotilag, Murtada K. Elbashir, Mahmood A. Mahmood, et al.
Processes (2025) Vol. 13, Iss. 5, pp. 1318-1318
Open Access
Attention-based cross domain graph neural network for prediction of drug–drug interactions
Hui Yu, KangKang Li, WenMin Dong, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 4
Closed Access | Times Cited: 10
Clinical trial recommendations using Semantics-Based inductive inference and knowledge graph embeddings
Murthy Devarakonda, Smita Mohanty, Raja Rao Sunkishala, et al.
Journal of Biomedical Informatics (2024) Vol. 154, pp. 104627-104627
Open Access | Times Cited: 3
Machine learning assisted photothermal conversion efficiency prediction of anticancer photothermal agents
Siwei Wu, Zhenxing Pan, Xiaojing Li, et al.
Chemical Engineering Science (2023) Vol. 273, pp. 118619-118619
Closed Access | Times Cited: 8
A multi-scale feature fusion model based on biological knowledge graph and transformer-encoder for drug-drug interaction prediction
Tao Wang, Qiang Deng, Jialu Hu, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 2
CoRTEx: contrastive learning for representing terms via explanations with applications on constructing biomedical knowledge graphs
Huaiyuan Ying, Zhengyun Zhao, Yang Zhao, et al.
Journal of the American Medical Informatics Association (2024) Vol. 31, Iss. 9, pp. 1912-1920
Open Access | Times Cited: 2
MathEagle: Accurate prediction of drug-drug interaction events via multi-head attention and heterogeneous attribute graph learning
Lin-Xuan Hou, Hai-Cheng Yi, Zhu‐Hong You, et al.
Computers in Biology and Medicine (2024) Vol. 177, pp. 108642-108642
Closed Access | Times Cited: 2
A spatial hierarchical network learning framework for drug repositioning allowing interpretation from macro to micro scale
Zhong-Hao Ren, Xiangxiang Zeng, Yizhen Lao, et al.
Communications Biology (2024) Vol. 7, Iss. 1
Open Access | Times Cited: 2
DDI-GPT: Explainable Prediction of Drug-Drug Interactions using Large Language Models enhanced with Knowledge Graphs
Chengqi Xu, Krishna C. Bulusu, Heng Pan, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 2
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