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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:

Matrix reconstruction with reliable neighbors for predicting potential MiRNA–disease associations
Hailin Feng, Dongdong Jin, Jian Li, et al.
Briefings in Bioinformatics (2022) Vol. 24, Iss. 1
Closed Access | Times Cited: 18

Showing 18 citing articles:

Joint masking and self-supervised strategies for inferring small molecule-miRNA associations
Zhecheng Zhou, Linlin Zhuo, Xiangzheng Fu, et al.
Molecular Therapy — Nucleic Acids (2023) Vol. 35, Iss. 1, pp. 102103-102103
Open Access | Times Cited: 29
Multi-task prediction-based graph contrastive learning for inferring the relationship among lncRNAs, miRNAs and diseases
Nan Sheng, Yan Wang, Lan Huang, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 5
Closed Access | Times Cited: 24
Identification of ferroptosis-related lncRNAs for predicting prognosis and immunotherapy response in non-small cell lung cancer
Lin Yuan, Shengguo Sun, Qinhu Zhang, et al.
Future Generation Computer Systems (2024) Vol. 159, pp. 204-220
Closed Access | Times Cited: 13
GEnDDn: An lncRNA–Disease Association Identification Framework Based on Dual-Net Neural Architecture and Deep Neural Network
Lihong Peng, Mengnan Ren, Liangliang Huang, et al.
Interdisciplinary Sciences Computational Life Sciences (2024) Vol. 16, Iss. 2, pp. 418-438
Closed Access | Times Cited: 7
Predicting miRNA-disease association via graph attention learning and multiplex adaptive modality fusion
Z M Jin, Minhui Wang, Chang Tang, et al.
Computers in Biology and Medicine (2023) Vol. 169, pp. 107904-107904
Closed Access | Times Cited: 13
Learning Association Characteristics by Dynamic Hypergraph and Gated Convolution Enhanced Pairwise Attributes for Prediction of Disease-Related lncRNAs
Ping Xuan, Siyuan Lu, Hui Cui, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 8, pp. 3569-3578
Closed Access | Times Cited: 4
A Brief Survey of Deep Learning-based Models for CircRNA-Protein Binding Sites Prediction
Zhen Shen, Lin Yuan, Wenzheng Bao, et al.
Neurocomputing (2025), pp. 129637-129637
Open Access
Gene-related multi-network collaborative deep feature learning for predicting miRNA-disease associations
Pengli Lu, Xu Cao
Computers & Electrical Engineering (2025) Vol. 123, pp. 110242-110242
Closed Access
PGCNMDA: Learning node representations along paths with graph convolutional network for predicting miRNA-disease associations
S. Chu, Guihua Duan, Cheng Yan
Methods (2024) Vol. 229, pp. 71-81
Closed Access | Times Cited: 3
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
DGAMDA: Predicting miRNA‐disease association based on dynamic graph attention network
Changxin Jia, Fuyu Wang, Baoxiang Xing, et al.
International Journal for Numerical Methods in Biomedical Engineering (2024) Vol. 40, Iss. 5
Closed Access | Times Cited: 1
Overcoming CRISPR-Cas9 off-target prediction hurdles: A novel approach with ESB rebalancing strategy and CRISPR-MCA model
Yanpeng Yang, Yanyi Zheng, Quan Zou, et al.
PLoS Computational Biology (2024) Vol. 20, Iss. 9, pp. e1012340-e1012340
Open Access | Times Cited: 1
MDformer: A transformer-based method for predicting miRNA-Disease associations using multi-source feature fusion and maximal meta-path instances encoding
Benzhi Dong, Weidong Sun, Dali Xu, et al.
Computers in Biology and Medicine (2023) Vol. 167, pp. 107585-107585
Closed Access | Times Cited: 3
Three-layer heterogeneous network based on the integration of CircRNA information for MiRNA-disease association prediction
Jia Qu, Shuting Liu, Han Li, et al.
PeerJ Computer Science (2024) Vol. 10, pp. e2070-e2070
Open Access
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
A Multi-Relational Graph Encoder Network for Fine-Grained Prediction of MiRNA-Disease Associations
Shengpeng Yu, Hong Wang, Jing Li, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2023) Vol. 21, Iss. 1, pp. 45-56
Closed Access | Times Cited: 1
Predicting miRNA-disease associations based on graph convolutional network with path learning
S. Chu, Guihua Duan, Cheng Yan
2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2023), pp. 474-479
Closed Access
Contrastive self-supervised graph convolutional network for detecting the relationship among lncRNAs, miRNAs, and diseases
Nan Sheng, Lan Huang, Yan Wang, et al.
2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2023), pp. 622-629
Closed Access
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