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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 circRNA-Disease Associations Based on Deep Matrix Factorization with Multi-source Fusion
Guobo Xie, Hui Chen, Yuping Sun, et al.
Interdisciplinary Sciences Computational Life Sciences (2021) Vol. 13, Iss. 4, pp. 582-594
Closed Access | Times Cited: 12

Showing 12 citing articles:

MPCLCDA: predicting circRNA–disease associations by using automatically selected meta-path and contrastive learning
Wei Liu, Ting Tang, Xu Lu, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 4
Closed Access | Times Cited: 35
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: 36
MHDMF: Prediction of miRNA–disease associations based on Deep Matrix Factorization with Multi-source Graph Convolutional Network
Ning Ai, Yong Liang, Hao-Laing Yuan, et al.
Computers in Biology and Medicine (2022) Vol. 149, pp. 106069-106069
Closed Access | Times Cited: 17
GSLCDA: An Unsupervised Deep Graph Structure Learning Method for Predicting CircRNA-Disease Association
Lei Wang, Zhengwei Li, Zhu‐Hong You, et al.
IEEE Journal of Biomedical and Health Informatics (2023) Vol. 28, Iss. 3, pp. 1742-1751
Closed Access | Times Cited: 8
SCCPMD: Probability matrix decomposition method subject to corrected similarity constraints for inferring long non-coding RNA–disease associations
Lieqing Lin, Ruibin Chen, Yinting Zhu, et al.
Frontiers in Microbiology (2023) Vol. 13
Open Access | Times Cited: 6
GDCL-NcDA: identifying non-coding RNA-disease associations via contrastive learning between deep graph learning and deep matrix factorization
Ning Ai, Yong Liang, Haoliang Yuan, et al.
BMC Genomics (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 6
Predicting associations between CircRNA and diseases through structure-aware graph transformer and path-integral convolution
Jinkai Wu, Pengli Lu, Wenqi Zhang
Analytical Biochemistry (2024) Vol. 692, pp. 115554-115554
Closed Access | Times Cited: 1
CircRNA-based therapeutics: Current opinions and clinical potential
Hanyuan Liu, Xuelin Yao, Ying Zhou, et al.
The Innovation Medicine (2024) Vol. 2, Iss. 3, pp. 100081-100081
Closed Access | Times Cited: 1
iEssLnc: quantitative estimation of lncRNA gene essentialities with meta-path-guided random walks on the lncRNA-protein interaction network
Yingying Zhang, De-Min Liang, Pu-Feng Du
Briefings in Bioinformatics (2023) Vol. 24, Iss. 3
Closed Access | Times Cited: 3
Machine Learning Based Liver Cancer Disease Prediction System Using Improved Extreme Gradient Boosting Algorithm
Ying Xiao
(2024), pp. 1-5
Closed Access
ICDFGF: Identification of potential circRNA-disease associations based on feature graph factorization
Yuchen Zhang, Xiujuan Lei, Zhengfeng Wang, et al.
(2024), pp. 1-10
Closed Access
Convolution Neural Networks Using Deep Matrix Factorization for Predicting circRNA-Disease Association
Zhihao Liu, Cunmei Ji, Jiancheng Ni, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2021) Vol. 20, Iss. 1, pp. 277-284
Closed Access | Times Cited: 4
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