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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 Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning
Bo-Wei Zhao, Zhu‐Hong You, Lun Hu, et al.
Cancers (2021) Vol. 13, Iss. 9, pp. 2111-2111
Open Access | Times Cited: 34

Showing 1-25 of 34 citing articles:

A survey of drug-target interaction and affinity prediction methods via graph neural networks
Yue Zhang, Yuqing Hu, Na Han, et al.
Computers in Biology and Medicine (2023) Vol. 163, pp. 107136-107136
Closed Access | Times Cited: 24
Leveraging pre-trained language models for mining microbiome-disease relationships
Nikitha Karkera, Sathwik Acharya, Sucheendra K. Palaniappan
BMC Bioinformatics (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 23
Machine Learning for Sequence and Structure-Based Protein–Ligand Interaction Prediction
Yunjiang Zhang, Shuyuan Li, Kong Meng, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 5, pp. 1456-1472
Closed Access | Times Cited: 9
A brief review of protein–ligand interaction prediction
Lingling Zhao, Yan Zhu, Junjie Wang, et al.
Computational and Structural Biotechnology Journal (2022) Vol. 20, pp. 2831-2838
Open Access | Times Cited: 33
iPiDA-GCN: Identification of piRNA-disease associations based on Graph Convolutional Network
Jialu Hou, Hang Wei, Bin Liu
PLoS Computational Biology (2022) Vol. 18, Iss. 10, pp. e1010671-e1010671
Open Access | Times Cited: 29
pLMSNOSite: an ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pre-trained protein language model
Pawel Pratyush, Suresh Pokharel, Hiroto Saigo, et al.
BMC Bioinformatics (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 16
SubMDTA: drug target affinity prediction based on substructure extraction and multi-scale features
Shourun Pan, Leiming Xia, Lei Xu, et al.
BMC Bioinformatics (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 16
Automatic disease prediction from human gut metagenomic data using boosting GraphSAGE
K. Syama, J. Angel Arul Jothi, Namita Khanna
BMC Bioinformatics (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 13
Drug repurposing and prediction of multiple interaction types via graph embedding
E. Amiri Souri, Alicia M. Chenoweth, Sophia N. Karagiannis, et al.
BMC Bioinformatics (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 13
MCL-DTI: using drug multimodal information and bi-directional cross-attention learning method for predicting drug–target interaction
Ying Qian, Xinyi Li, Jian Wu, et al.
BMC Bioinformatics (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 10
Advancing drug–target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining
Warith Eddine Djeddi, Khalil Hermi, Sadok Ben Yahia, et al.
BMC Bioinformatics (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 10
Development and comparison of machine learning-based models for predicting heart failure after acute myocardial infarction
Xuewen Li, Chengming Shang, Changyan Xu, et al.
BMC Medical Informatics and Decision Making (2023) Vol. 23, Iss. 1
Open Access | Times Cited: 9
RLFDDA: a meta-path based graph representation learning model for drug–disease association prediction
Meng-Long Zhang, Bo-Wei Zhao, Xiaorui Su, et al.
BMC Bioinformatics (2022) Vol. 23, Iss. 1
Open Access | Times Cited: 13
A highly accurate delta check method using deep learning for detection of sample mix-up in the clinical laboratory
Rui Zhou, Yufang Liang, Hua-li Cheng, et al.
Clinical Chemistry and Laboratory Medicine (CCLM) (2021) Vol. 60, Iss. 12, pp. 1984-1992
Open Access | Times Cited: 14
Emerging technologies for drug repurposing: Harnessing the potential of text and graph embedding approaches
Xialan Dong, Weifan Zheng
Artificial Intelligence Chemistry (2024) Vol. 2, Iss. 1, pp. 100060-100060
Open Access | Times Cited: 1
Exploring potential circRNA biomarkers for cancers based on double-line heterogeneous graph representation learning
Yi Zhang, ZhenMei Wang, Hanyan Wei, et al.
BMC Medical Informatics and Decision Making (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 1
A Multi-graph Deep Learning Model for Predicting Drug-Disease Associations
Bo-Wei Zhao, Zhu‐Hong You, Lun Hu, et al.
Lecture notes in computer science (2021), pp. 580-590
Closed Access | Times Cited: 11
GraphCPIs: A novel graph-based computational model for potential compound-protein interactions
Zhan‐Heng Chen, Bo-Wei Zhao, Jianqiang Li, et al.
Molecular Therapy — Nucleic Acids (2023) Vol. 32, pp. 721-728
Open Access | Times Cited: 3
DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissues
Roohallah Mahdi‐Esferizi, Behnaz Haji Molla Hoseyni, Amir Mehrpanah, et al.
BMC Bioinformatics (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 3
Predicting Drug-Disease Associations via Meta-path Representation Learning based on Heterogeneous Information Net works
Meng-Long Zhang, Bo-Wei Zhao, Lun Hu, et al.
Lecture notes in computer science (2022), pp. 220-232
Closed Access | Times Cited: 5
Prediction of drug–target interactions through multi-task learning
Chaeyoung Moon, Dongsup Kim
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 5
Gtie-Rt: A comprehensive graph learning model for predicting drugs targeting metabolic pathways in human
Hayat Ali Shah, Juan Liu, Zhihui Yang
Journal of Bioinformatics and Computational Biology (2024) Vol. 22, Iss. 03
Closed Access
Ion-pumping microbial rhodopsin protein classification by machine learning approach
S. Muthu Krishnan, Anamika Thakur, Manoj Kumar, et al.
BMC Bioinformatics (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 1
Computational method for aromatase-related proteins using machine learning approach
S. Muthu Krishnan, Jasmeet Kaur
PLoS ONE (2023) Vol. 18, Iss. 3, pp. e0283567-e0283567
Open Access | Times Cited: 1
Predicting Large-scale Protein-protein Interactions by Extracting Coevolutionary Patterns with MapReduce Paradigm
Lun Hu, Bo-Wei Zhao, Shicheng Yang, et al.
2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (2021), pp. 939-944
Closed Access | Times Cited: 3
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