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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:

Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction
Jin Li, Sai Zhang, Tao Liu, et al.
Bioinformatics (2019) Vol. 36, Iss. 8, pp. 2538-2546
Closed Access | Times Cited: 268

Showing 1-25 of 268 citing articles:

Predicting drug–disease associations through layer attention graph convolutional network
Zhouxin Yu, Feng Huang, Xiaohan Zhao, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 4
Closed Access | Times Cited: 278
Graph Neural Networks and Their Current Applications in Bioinformatics
Xiaomeng Zhang, Liang Li, Lin Liu, et al.
Frontiers in Genetics (2021) Vol. 12
Open Access | Times Cited: 214
DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence
Qianqian Song, Jing Su
Briefings in Bioinformatics (2020) Vol. 22, Iss. 5
Open Access | Times Cited: 164
Graph representation learning in bioinformatics: trends, methods and applications
Hai-Cheng Yi, Zhu‐Hong You, De-Shuang Huang, et al.
Briefings in Bioinformatics (2021) Vol. 23, Iss. 1
Closed Access | Times Cited: 137
Drug repositioning based on the heterogeneous information fusion graph convolutional network
Lijun Cai, Changcheng Lu, Junlin Xu, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 6
Closed Access | Times Cited: 122
Multi-view Multichannel Attention Graph Convolutional Network for miRNA–disease association prediction
Xinru Tang, Jiawei Luo, Cong Shen, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 6
Closed Access | Times Cited: 114
A weighted bilinear neural collaborative filtering approach for drug repositioning
Yajie Meng, Changcheng Lu, Min Jin, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 2
Closed Access | Times Cited: 114
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: 89
Updated review of advances in microRNAs and complex diseases: experimental results, databases, webservers and data fusion
Huang Li, Li Zhang, Xing Chen
Briefings in Bioinformatics (2022) Vol. 23, Iss. 6
Closed Access | Times Cited: 76
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: 73
Fusing Higher and Lower-Order Biological Information for Drug Repositioning via Graph Representation Learning
Bo-Wei Zhao, Lei Wang, Pengwei Hu, et al.
IEEE Transactions on Emerging Topics in Computing (2023) Vol. 12, Iss. 1, pp. 163-176
Closed Access | Times Cited: 60
Variational gated autoencoder-based feature extraction model for inferring disease-miRNA associations based on multiview features
Yanbu Guo, Dongming Zhou, Xiaoli Ruan, et al.
Neural Networks (2023) Vol. 165, pp. 491-505
Closed Access | Times Cited: 55
AMHMDA: attention aware multi-view similarity networks and hypergraph learning for miRNA–disease associations identification
Ning Qiao, Yaomiao Zhao, Jun Gao, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 2
Closed Access | Times Cited: 47
Motif-Aware miRNA-Disease Association Prediction via Hierarchical Attention Network
Bo-Wei Zhao, Yi-Zhou He, Xiaorui Su, et al.
IEEE Journal of Biomedical and Health Informatics (2024) Vol. 28, Iss. 7, pp. 4281-4294
Closed Access | Times Cited: 17
A graph auto-encoder model for miRNA-disease associations prediction
Zhengwei Li, Jiashu Li, Ru Nie, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 4
Closed Access | Times Cited: 96
Variational graph auto-encoders for miRNA-disease association prediction
Yulian Ding, Li‐Ping Tian, Xiujuan Lei, et al.
Methods (2020) Vol. 192, pp. 25-34
Closed Access | Times Cited: 83
scGCN is a graph convolutional networks algorithm for knowledge transfer in single cell omics
Qianqian Song, Jing Su, Wei Zhang
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 82
MVGCN: data integration through multi-view graph convolutional network for predicting links in biomedical bipartite networks
Haitao Fu, Feng Huang, Xuan Liu, et al.
Bioinformatics (2021) Vol. 38, Iss. 2, pp. 426-434
Closed Access | Times Cited: 77
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: 76
GraphCDR: a graph neural network method with contrastive learning for cancer drug response prediction
Xuan Liu, Congzhi Song, Feng Huang, et al.
Briefings in Bioinformatics (2021) Vol. 23, Iss. 1
Closed Access | Times Cited: 70
Aspect-level sentiment analysis: A survey of graph convolutional network methods
Huyen Trang Phan, Ngoc Thanh Nguyên, Dosam Hwang
Information Fusion (2022) Vol. 91, pp. 149-172
Closed Access | Times Cited: 68
A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations
Zhuangwei Shi, Han Zhang, Chen Jin, et al.
BMC Bioinformatics (2021) Vol. 22, Iss. 1
Open Access | Times Cited: 67
MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph
Yanyi Chu, Xuhong Wang, Qiuying Dai, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 6
Closed Access | Times Cited: 62
IMCHGAN: Inductive Matrix Completion With Heterogeneous Graph Attention Networks for Drug-Target Interactions Prediction
Jin Li, Jingru Wang, Hao Lv, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2021) Vol. 19, Iss. 2, pp. 655-665
Closed Access | Times Cited: 61
Predicting miRNA–disease associations via learning multimodal networks and fusing mixed neighborhood information
Zhengzheng Lou, Zhaoxu Cheng, Hui Li, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 5
Closed Access | Times Cited: 59
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