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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:

MSGCL: inferring miRNA–disease associations based on multi-view self-supervised graph structure contrastive learning
Xinru Ruan, Changzhi Jiang, Peixuan Lin, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 2
Closed Access | Times Cited: 19

Showing 19 citing articles:

A Survey of Deep Learning for Detecting miRNA- Disease Associations: Databases, Computational Methods, Challenges, and Future Directions
Nan Sheng, Xuping Xie, Yan Wang, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2024) Vol. 21, Iss. 3, pp. 328-347
Closed Access | Times Cited: 9
Predicting miRNA–Disease Associations by Combining Graph and Hypergraph Convolutional Network
Xujun Liang, Ming Guo, Longying Jiang, et al.
Interdisciplinary Sciences Computational Life Sciences (2024) Vol. 16, Iss. 2, pp. 289-303
Closed Access | Times Cited: 8
MHCLMDA: multihypergraph contrastive learning for miRNA–disease association prediction
Wei Peng, Zhichen He, Wei Dai, et al.
Briefings in Bioinformatics (2023) Vol. 25, Iss. 1
Open Access | Times Cited: 19
MGCNSS: miRNA–disease association prediction with multi-layer graph convolution and distance-based negative sample selection strategy
Zhen Tian, Chenguang Han, Lewen Xu, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 3
Open Access | Times Cited: 5
HGCLAMIR: Hypergraph contrastive learning with attention mechanism and integrated multi-view representation for predicting miRNA-disease associations
Dong Ouyang, Yong Liang, Jinfeng Wang, et al.
PLoS Computational Biology (2024) Vol. 20, Iss. 4, pp. e1011927-e1011927
Open Access | Times Cited: 5
Subgraph Topology and Dynamic Graph Topology Enhanced Graph Learning and Pairwise Feature Context Relationship Integration for Predicting Disease-Related miRNAs
Ping Xuan, Xiaoying Qi, Sentao Chen, et al.
Journal of Chemical Information and Modeling (2025)
Closed Access
MGHSTCKW: Predicting miRNA-drug sensitivity association using hypergraph sparse transformer and hypergraph-induced contrastive learning based on meta-path
Dong Ouyang, Bo Jin, Jing Tian, et al.
Expert Systems with Applications (2025), pp. 126879-126879
Closed Access
Self-supervised learning for graph-structured data in healthcare applications: A comprehensive review
Safa Ben Atitallah, Chaima Ben Rabah, Maha Driss, et al.
Computers in Biology and Medicine (2025) Vol. 188, pp. 109874-109874
Open Access
RNA Sequence Analysis Landscape: A Comprehensive Review of Task Types, Databases, Datasets, Word Embedding Methods, and Language Models
Muhammad Nabeel Asim, Muhammad Ali Ibrahim, Tayyaba Asif, et al.
Heliyon (2025) Vol. 11, Iss. 2, pp. e41488-e41488
Open Access
Novel Two-Stage, Contrastive Learning for Predicting the Phenotype of Metabolic Dysfunction-Associated Fatty Liver among Adults: Method Development and Evaluation (Preprint)
Sizhe Jasmine Chen, Da Xu, Derek K. Hu, et al.
(2025)
Closed Access
PMDAGS: Predicting miRNA-Disease Associations With Graph Nonlinear Diffusion Convolution Network and Similarities
Cheng Yan, Guihua Duan
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2024) Vol. 21, Iss. 3, pp. 394-404
Closed Access | Times Cited: 2
A comprehensive review and evaluation of graph neural networks for non-coding RNA and complex disease associations
Xiaowen Hu, Dayun Liu, Jiaxuan Zhang, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 6
Closed Access | Times Cited: 4
MNCLCDA: predicting circRNA-drug sensitivity associations by using mixed neighbourhood information and contrastive learning
Guanghui Li, Feifan Zeng, Jiawei Luo, et al.
BMC Medical Informatics and Decision Making (2023) Vol. 23, Iss. 1
Open Access | Times Cited: 4
RSANMDA: Resampling based subview attention network for miRNA-disease association prediction
Longfei Luo, Zhuokun Tan, Shunfang Wang
Methods (2024) Vol. 230, pp. 99-107
Closed Access | Times Cited: 1
Drug-target interaction prediction with collaborative contrastive learning and adaptive self-paced sampling strategy
Zhen Tian, Yue Yu, Fengming Ni, et al.
BMC Biology (2024) Vol. 22, Iss. 1
Open Access | Times Cited: 1
DEJKMDR: miRNA-disease association prediction method based on graph convolutional network
Shiyuan Gao, Zhufang Kuang, Tao Duan, et al.
Frontiers in Medicine (2023) Vol. 10
Open Access | Times Cited: 2
Prediction of Potential miRNA-Disease Associations Based on a Masked Graph Autoencoder
Hailin Feng, Chenchen Ke, Quan Zou, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2024) Vol. 21, Iss. 6, pp. 1874-1885
Closed Access
Prediction of miRNA-disease association based on heterogeneous hypergraph convolution and heterogeneous graph multi-scale convolution
Wei Dai, S.K. Pang, Zhichen He, et al.
Health Information Science and Systems (2024) Vol. 13, Iss. 1
Closed Access
CLPiDA: A Contrastive Learning Approach for Predicting Potential PiRNA-Disease Associations
Xiaowen Hu, Yuanpeng Zhang, Jiaxuan Zhang, et al.
2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2023) Vol. 33, pp. 159-164
Closed Access | Times Cited: 1
Metapath-aggregated multilevel graph embedding for miRNA‒disease association prediction
Wen Cao, Chen Yang, Jianye Yang, et al.
2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2023), pp. 468-473
Closed Access | Times Cited: 1
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