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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:

Ensemble of decision tree reveals potential miRNA-disease associations
Xing Chen, Chi-Chi Zhu, Jun Yin
PLoS Computational Biology (2019) Vol. 15, Iss. 7, pp. e1007209-e1007209
Open Access | Times Cited: 186

Showing 1-25 of 186 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: 581
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
A deep learning method for predicting metabolite–disease associations via graph neural network
Feiyue Sun, Jianqiang Sun, Qi Zhao
Briefings in Bioinformatics (2022) Vol. 23, Iss. 4
Closed Access | Times Cited: 193
Predicting the potential human lncRNA–miRNA interactions based on graph convolution network with conditional random field
Wenya Wang, Li Zhang, Jianqiang Sun, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 6
Closed Access | Times Cited: 186
Investigating cardiotoxicity related with hERG channel blockers using molecular fingerprints and graph attention mechanism
Tianyi Wang, Jianqiang Sun, Qi Zhao
Computers in Biology and Medicine (2022) Vol. 153, pp. 106464-106464
Closed Access | Times Cited: 161
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: 136
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
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
Identification of miRNA–disease associations via deep forest ensemble learning based on autoencoder
Wei Liu, Hui Lin, Huang Li, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 3
Closed Access | Times Cited: 87
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
Predicting CircRNA-Disease Associations via Feature Convolution Learning With Heterogeneous Graph Attention Network
Peng Li, Yang Cheng, Yi‐Fan Chen, et al.
IEEE Journal of Biomedical and Health Informatics (2023) Vol. 27, Iss. 6, pp. 3072-3082
Closed Access | Times Cited: 41
Identification of human microRNA-disease association via low-rank approximation-based link propagation and multiple kernel learning
Yizheng Wang, Xin Zhang, Ying Ju, et al.
Frontiers of Computer Science (2024) Vol. 18, Iss. 2
Closed Access | Times Cited: 15
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
An Improved Anticancer Drug-Response Prediction Based on an Ensemble Method Integrating Matrix Completion and Ridge Regression
Chuanying Liu, Wei Dong, Ju Xiang, et al.
Molecular Therapy — Nucleic Acids (2020) Vol. 21, pp. 676-686
Open Access | Times Cited: 80
Predicting potential miRNA-disease associations by combining gradient boosting decision tree with logistic regression
S. Zhou, Shulin Wang, Qi Wu, et al.
Computational Biology and Chemistry (2020) Vol. 85, pp. 107200-107200
Closed Access | Times Cited: 79
Construct a circRNA/miRNA/mRNA regulatory network to explore potential pathogenesis and therapy options of clear cell renal cell carcinoma
Shuheng Bai, Yinying Wu, Yanli Yan, et al.
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 74
Cell–cell communication inference and analysis in the tumour microenvironments from single-cell transcriptomics: data resources and computational strategies
Lihong Peng, Feixiang Wang, Zhao Wang, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 4
Closed 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
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
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
Predicting potential interactions between lncRNAs and proteins via combined graph auto-encoder methods
Jingxuan Zhao, Jianqiang Sun, Stella C. Shuai, et al.
Briefings in Bioinformatics (2022) Vol. 24, Iss. 1
Closed Access | Times Cited: 43
NCMD: Node2vec-Based Neural Collaborative Filtering for Predicting MiRNA-Disease Association
Jihwan Ha, Sanghyun Park
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2022) Vol. 20, Iss. 2, pp. 1257-1268
Open Access | Times Cited: 40
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
SMAP: Similarity-based matrix factorization framework for inferring miRNA-disease association
Jihwan Ha
Knowledge-Based Systems (2023) Vol. 263, pp. 110295-110295
Closed Access | Times Cited: 34
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