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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:

Predicting cell line-specific synergistic drug combinations through a relational graph convolutional network with attention mechanism
Peng Zhang, Shikui Tu, Wen Zhang, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 6
Closed Access | Times Cited: 22

Showing 22 citing articles:

Attention is all you need: utilizing attention in AI-enabled drug discovery
Yang Zhang, Caiqi Liu, Mujiexin Liu, et al.
Briefings in Bioinformatics (2023) Vol. 25, Iss. 1
Open Access | Times Cited: 102
Predicting drug-induced liver injury using graph attention mechanism and molecular fingerprints
Jifeng Wang, Li Zhang, Jianqiang Sun, et al.
Methods (2023) Vol. 221, pp. 18-26
Open Access | Times Cited: 28
MGAE-DC: Predicting the synergistic effects of drug combinations through multi-channel graph autoencoders
Peng Zhang, Shikui Tu
PLoS Computational Biology (2023) Vol. 19, Iss. 3, pp. e1010951-e1010951
Open Access | Times Cited: 19
New methods for drug synergy prediction: A mini-review
Fatemeh Abbasi, Juho Rousu
Current Opinion in Structural Biology (2024) Vol. 86, pp. 102827-102827
Open Access | Times Cited: 3
A review on graph neural networks for predicting synergistic drug combinations
Milad Besharatifard, Fatemeh Vafaee
Artificial Intelligence Review (2024) Vol. 57, Iss. 3
Open Access | Times Cited: 3
MFSynDCP: multi-source feature collaborative interactive learning for drug combination synergy prediction
Yunyun Dong, Yunqing Chang, Yu-Xiang Wang, et al.
BMC Bioinformatics (2024) Vol. 25, Iss. 1
Open Access | Times Cited: 3
Machine learning model for anti-cancer drug combinations: Analysis, prediction, and validation
Jingbo Zhou, Dongyang Tang, Lin He, et al.
Pharmacological Research (2023) Vol. 194, pp. 106830-106830
Open Access | Times Cited: 9
Interpreting the Mechanism of Synergism for Drug Combinations Using Attention-Based Hierarchical Graph Pooling
Zehao Dong, Heming Zhang, Yixin Chen, et al.
Cancers (2023) Vol. 15, Iss. 17, pp. 4210-4210
Open Access | Times Cited: 9
MPFFPSDC: A multi-pooling feature fusion model for predicting synergistic drug combinations
Xin Bao, Jianqiang Sun, Ming Yi, et al.
Methods (2023) Vol. 217, pp. 1-9
Closed Access | Times Cited: 6
RedCDR: Dual Relation Distillation for Cancer Drug Response Prediction
Muhao Xu, Zhenfeng Zhu, Yawei Zhao, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2024) Vol. 21, Iss. 5, pp. 1468-1479
Closed Access | Times Cited: 1
Deep learning for predicting synergistic drug combinations: State‐of‐the‐arts and future directions
Yu Wang, Junjie Wang, Yun Liu
Clinical and Translational Discovery (2024) Vol. 4, Iss. 3
Open Access | Times Cited: 1
A knowledge graph embedding-based method for predicting the synergistic effects of drug combinations
Peng Zhang, Shikui Tu
2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2022), pp. 1974-1981
Closed Access | Times Cited: 7
MATT-DDI: Predicting multi-type drug-drug interactions via heterogeneous attention mechanisms
Shenggeng Lin, Xueying Mao, Liang Hong, et al.
Methods (2023) Vol. 220, pp. 1-10
Closed Access | Times Cited: 3
Interpreting the Mechanism of Synergism for Drug Combinations Using Attention-Based Hierarchical Graph Pooling
Zehao Dong, Heming Zhang, Yixin Chen, et al.
arXiv (Cornell University) (2022)
Open Access | Times Cited: 4
SDDSynergy: Learning Important Molecular Substructures for Explainable Anticancer Drug Synergy Prediction
Y. H. Liu, Peiliang Zhang, Chao Che, et al.
Journal of Chemical Information and Modeling (2024)
Closed Access
DSA-DeepFM: A Dual-Stage Attention-Enhanced DeepFM Model for Predicting Anticancer Synergistic Drug Combinations
Yuexi Gu, Yanhui Sun, Louxin Zhang, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Predicting Cell Line-Specific Synergistic Drug Combinations Through Siamese Network with Attention Mechanism
Xin Bao, XiangYong Chen, Jianlong Qiu, et al.
Communications in computer and information science (2024), pp. 87-95
Closed Access
DualSyn: A dual-level feature interaction method to predict synergistic drug combinations
Zehui Chen, Zimeng Li, Xiangzhen Shen, et al.
Expert Systems with Applications (2024) Vol. 257, pp. 125065-125065
Closed Access
Graph neural network and multi-data heterogeneous networks for microbe-disease prediction
Houwu Gong, Xiong You, Min Jin, et al.
Frontiers in Microbiology (2022) Vol. 13
Open Access | Times Cited: 3
A Review on Graph Neural Networks for Predicting Synergistic Drug Combinations
Milad Besharatifard, Fatemeh Vafaee
Research Square (Research Square) (2023)
Open Access
Continuous Prompt for Chemical Language Model Aided Anticancer Synergistic Drug Combination Prediction
Guannan Geng, Lingling Zhao, Chunyu Wang, et al.
2021 IEEE International Conference on Big Data (Big Data) (2023), pp. 4406-4412
Closed Access
HUF-DCIE: A Hypergraph Based Unified Framework to Predict Drug Combination Integrated Effects
Liqiao Yan, Yi Cao
(2023) Vol. 288, pp. 274-279
Closed Access
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