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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:

NCMCMDA: miRNA–disease association prediction through neighborhood constraint matrix completion
Xing Chen, Lian-Gang Sun, Yan Zhao
Briefings in Bioinformatics (2019) Vol. 22, Iss. 1, pp. 485-496
Closed Access | Times Cited: 179

Showing 26-50 of 179 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
Tensor decomposition with relational constraints for predicting multiple types of microRNA-disease associations
Feng Huang, Xiang Yue, Zhankun Xiong, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 3
Open Access | Times Cited: 67
SCMFMDA: Predicting microRNA-disease associations based on similarity constrained matrix factorization
Lei Li, Zhen Gao, Yutian Wang, et al.
PLoS Computational Biology (2021) Vol. 17, Iss. 7, pp. e1009165-e1009165
Open 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
Predicting miRNA-disease associations based on graph attention network with multi-source information
Guanghui Li, Tao Fang, Yuejin Zhang, et al.
BMC Bioinformatics (2022) Vol. 23, Iss. 1
Open Access | Times Cited: 29
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: 20
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 associations using an ensemble learning framework with resampling method
Qiguo Dai, Zhaowei Wang, Ziqiang Liu, et al.
Briefings in Bioinformatics (2021) Vol. 23, Iss. 1
Closed Access | Times Cited: 34
MLMD: Metric Learning for Predicting MiRNA-Disease Associations
Jihwan Ha, Chihyun Park
IEEE Access (2021) Vol. 9, pp. 78847-78858
Open Access | Times Cited: 33
PDMDA: predicting deep-level miRNA–disease associations with graph neural networks and sequence features
Cheng Yan, Guihua Duan, Na Li, et al.
Bioinformatics (2022) Vol. 38, Iss. 8, pp. 2226-2234
Open Access | Times Cited: 28
Identidication of novel biomarkers in non-small cell lung cancer using machine learning
Fangwei Wang, Qisheng Su, Chaoqian Li
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 28
Predicting miRNA-Disease Associations From miRNA-Gene-Disease Heterogeneous Network With Multi-Relational Graph Convolutional Network Model
Wei Peng, Zicheng Che, Wei Dai, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2022) Vol. 20, Iss. 6, pp. 3363-3375
Closed Access | Times Cited: 27
Predicting miRNA-disease associations based on lncRNA–miRNA interactions and graph convolution networks
Wengang Wang, Hailin Chen
Briefings in Bioinformatics (2022) Vol. 24, Iss. 1
Closed Access | Times Cited: 23
Cuproptosis-related lncRNA signature for prognostic prediction in patients with acute myeloid leukemia
Yidong Zhu, Jun He, Zihua Li, et al.
BMC Bioinformatics (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 16
Circular RNAs: implications of signaling pathways and bioinformatics in human cancer
Fan Hu, Yin Peng, Xinmin Fan, et al.
Cancer Biology and Medicine (2023) Vol. 20, Iss. 2, pp. 104-128
Open Access | Times Cited: 15
Finding potential lncRNA–disease associations using a boosting-based ensemble learning model
Liqian Zhou, Xinhuai Peng, Lijun Zeng, et al.
Frontiers in Genetics (2024) Vol. 15
Open Access | Times Cited: 6
MUSCLE: multi-view and multi-scale attentional feature fusion for microRNA–disease associations prediction
Boya Ji, Haitao Zou, Li‐Wen Xu, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 3
Open Access | Times Cited: 5
SCPLPA: An miRNA–disease association prediction model based on spatial consistency projection and label propagation algorithm
Min Chen, Yingwei Deng, Zejun Li, et al.
Journal of Cellular and Molecular Medicine (2024) Vol. 28, Iss. 9
Open Access | Times Cited: 5
A Semi-Supervised Learning Method for MiRNA-Disease Association Prediction Based on Variational Autoencoder
Cunmei Ji, Yutian Wang, Zhen Gao, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2021) Vol. 19, Iss. 4, pp. 2049-2059
Closed Access | Times Cited: 28
Prediction of miRNA-disease associations in microbes based on graph convolutional networks and autoencoders
Qingquan Liao, Yuxiang Ye, Zihang Li, et al.
Frontiers in Microbiology (2023) Vol. 14
Open Access | Times Cited: 13
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
Predicting Lactobacillus delbrueckii subsp. bulgaricus-Streptococcus thermophilus interactions based on a highly accurate semi-supervised learning method
Shujuan Yang, Mei Bai, Weichi Liu, et al.
Science China Life Sciences (2024)
Closed Access | Times Cited: 4
MDA-CF: Predicting MiRNA-Disease associations based on a cascade forest model by fusing multi-source information
Qiuying Dai, Yanyi Chu, Zhiqi Li, et al.
Computers in Biology and Medicine (2021) Vol. 136, pp. 104706-104706
Closed Access | Times Cited: 26
Computational drug repositioning using similarity constrained weight regularization matrix factorization: A case of COVID‐19
Junlin Xu, Yajie Meng, Lihong Peng, et al.
Journal of Cellular and Molecular Medicine (2022) Vol. 26, Iss. 13, pp. 3772-3782
Open Access | Times Cited: 19
Predicting miRNA-disease associations based on PPMI and attention network
Xuping Xie, Yan Wang, Kai He, et al.
BMC Bioinformatics (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 10

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