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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:

iCircDA-MF: identification of circRNA-disease associations based on matrix factorization
Hang Wei, Bin Liu
Briefings in Bioinformatics (2019) Vol. 21, Iss. 4, pp. 1356-1367
Closed Access | Times Cited: 142

Showing 1-25 of 142 citing articles:

MicroRNAs and complex diseases: from experimental results to computational models
Xing Chen, Di Xie, Qi Zhao, et al.
Briefings in Bioinformatics (2017) Vol. 20, Iss. 2, pp. 515-539
Closed Access | Times Cited: 587
Circular RNAs and complex diseases: from experimental results to computational models
Chun-Chun Wang, Chendi Han, Qi Zhao, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 6
Open Access | Times Cited: 139
Updated review of advances in microRNAs and complex diseases: taxonomy, trends and challenges of computational models
Huang Li, Li Zhang, Xing Chen
Briefings in Bioinformatics (2022) Vol. 23, Iss. 5
Closed Access | Times Cited: 91
Updated review of advances in microRNAs and complex diseases: towards systematic evaluation of computational models
Huang Li, Li Zhang, Xing Chen
Briefings in Bioinformatics (2022) Vol. 23, Iss. 6
Closed Access | Times Cited: 77
GMNN2CD: identification of circRNA–disease associations based on variational inference and graph Markov neural networks
Mengting Niu, Quan Zou, Chunyu Wang
Bioinformatics (2022) Vol. 38, Iss. 8, pp. 2246-2253
Closed Access | Times Cited: 74
Predicting circRNA–Disease Associations through Multisource Domain-Aware Embeddings and Feature Projection Networks
Shuai Liang, Lei Wang, Zhu-Hong You, et al.
Journal of Chemical Information and Modeling (2025)
Closed Access | Times Cited: 3
DeepM6ASeq-EL: prediction of human N6-methyladenosine (m6A) sites with LSTM and ensemble learning
Juntao Chen, Quan Zou, Jing Li
Frontiers of Computer Science (2021) Vol. 16, Iss. 2
Closed Access | Times Cited: 80
Deep Matrix Factorization Improves Prediction of Human CircRNA-Disease Associations
Chengqian Lu, Min Zeng, Fuhao Zhang, et al.
IEEE Journal of Biomedical and Health Informatics (2020) Vol. 25, Iss. 3, pp. 891-899
Closed Access | Times Cited: 77
Exploring associations of non-coding RNAs in human diseases via three-matrix factorization with hypergraph-regular terms on center kernel alignment
Hao Wang, Jijun Tang, Yijie Ding, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 5
Closed Access | Times Cited: 73
KGANCDA: predicting circRNA-disease associations based on knowledge graph attention network
Wei Lan, Yi Dong, Qingfeng Chen, et al.
Briefings in Bioinformatics (2021) Vol. 23, Iss. 1
Closed Access | Times Cited: 62
RNMFLP: Predicting circRNA–disease associations based on robust nonnegative matrix factorization and label propagation
Peng Li, Yang Cheng, Huang Li, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 5
Closed Access | Times Cited: 56
Deep learning models for disease-associated circRNA prediction: a review
Yaojia Chen, Jiacheng Wang, Chuyu Wang, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 6
Closed Access | Times Cited: 39
A computational model of circRNA-associated diseases based on a graph neural network: prediction and case studies for follow-up experimental validation
Mengting Niu, Chunyu Wang, Zhanguo Zhang, et al.
BMC Biology (2024) Vol. 22, Iss. 1
Open Access | Times Cited: 15
LGCDA: Predicting CircRNA-Disease Association Based on Fusion of Local and Global Features
Wei Lan, Chunling Li, Qingfeng Chen, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2024) Vol. 21, Iss. 5, pp. 1413-1422
Closed Access | Times Cited: 13
Supervised contrastive knowledge graph learning for ncRNA–disease association prediction
Yan Wang, Xuping Xie, Ye Wang, et al.
Expert Systems with Applications (2025) Vol. 269, pp. 126257-126257
Closed Access | Times Cited: 1
Predicting human microbe–disease associations via graph attention networks with inductive matrix completion
Yahui Long, Jiawei Luo, Yu Zhang, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 3
Closed Access | Times Cited: 69
iCircRBP-DHN: identification of circRNA-RBP interaction sites using deep hierarchical network
Yuning Yang, Zilong Hou, Zhiqiang Ma, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 4
Closed Access | Times Cited: 67
LDGRNMF: LncRNA-disease associations prediction based on graph regularized non-negative matrix factorization
Mei-Neng Wang, Zhu‐Hong You, Lei Wang, et al.
Neurocomputing (2020) Vol. 424, pp. 236-245
Closed Access | Times Cited: 62
Fold-LTR-TCP: protein fold recognition based on triadic closure principle
Bin Liu, Yulin Zhu, Ke Yan
Briefings in Bioinformatics (2019) Vol. 21, Iss. 6, pp. 2185-2193
Closed Access | Times Cited: 61
Integrating random walk with restart and k-Nearest Neighbor to identify novel circRNA-disease association
Xiujuan Lei, Chen Bian
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 56
MGRCDA: Metagraph Recommendation Method for Predicting CircRNA–Disease Association
Lei Wang, Zhu‐Hong You, De-Shuang Huang, et al.
IEEE Transactions on Cybernetics (2021) Vol. 53, Iss. 1, pp. 67-75
Closed Access | Times Cited: 56
A comprehensive survey on computational methods of non-coding RNA and disease association prediction
Xiujuan Lei, Thosini Bamunu Mudiyanselage, Yuchen Zhang, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 4
Closed Access | Times Cited: 52
Advances in the Identification of Circular RNAs and Research Into circRNAs in Human Diseases
Shihu Jiao, Song Wu, Shan Huang, et al.
Frontiers in Genetics (2021) Vol. 12
Open Access | Times Cited: 47
SGANRDA: semi-supervised generative adversarial networks for predicting circRNA–disease associations
Lei Wang, Xin Yan, Zhu‐Hong You, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 5
Closed Access | Times Cited: 43
iGRLCDA: identifying circRNA–disease association based on graph representation learning
Han-Yuan Zhang, Lei Wang, Zhu‐Hong You, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 3
Closed Access | Times Cited: 36
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