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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:

LncRNA-disease association identification using graph auto-encoder and learning to rank
Liang Qi, Wenxiang Zhang, Hao Wu, et al.
Briefings in Bioinformatics (2022) Vol. 24, Iss. 1
Open Access | Times Cited: 15

Showing 15 citing articles:

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
Predicting RNA structures and functions by artificial intelligence
Jun Zhang, Mei Lang, Yaoqi Zhou, et al.
Trends in Genetics (2023) Vol. 40, Iss. 1, pp. 94-107
Closed Access | Times Cited: 18
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
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: 5
LncRNA in gastric cancer drug resistance: deciphering the therapeutic strategies
Weiling Liu, WeiFa Wang
Frontiers in Oncology (2025) Vol. 15
Open Access
Predicting lncRNA-disease associations using multiple metapaths in hierarchical graph attention networks
Dengju Yao, Yuexiao Deng, Xiaojuan Zhan, et al.
BMC Bioinformatics (2024) Vol. 25, Iss. 1
Open Access | Times Cited: 3
Developing explainable models for lncRNA-Targeted drug discovery using graph autoencoders
Huiying Liu, Xiangzheng Fu, Haiting Chen, et al.
Future Generation Computer Systems (2024) Vol. 160, pp. 29-39
Closed Access | Times Cited: 3
DAmiRLocGNet: miRNA subcellular localization prediction by combining miRNA–disease associations and graph convolutional networks
Tao Bai, Ke Yan, Bin Liu
Briefings in Bioinformatics (2023) Vol. 24, Iss. 4
Closed Access | Times Cited: 8
Similarity-guided Graph Contrastive Learning for LncRNA-disease Association Prediction
Qingfeng Chen, Junlai Qiu, Wei Lan, et al.
Journal of Molecular Biology (2024), pp. 168609-168609
Closed Access | Times Cited: 2
Identifying Associations between Small Nucleolar RNAs and Diseases via Graph Convolutional Network and Attention Mechanism
Shuchen Liu, Wen Zhu, Peng Wang, et al.
IEEE Journal of Biomedical and Health Informatics (2024) Vol. 28, Iss. 12, pp. 7647-7658
Closed Access | Times Cited: 2
A comprehensive review and evaluation of graph neural networks for non-coding RNA and complex disease associations
Xiaowen Hu, Dayun Liu, Jiaxuan Zhang, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 6
Closed Access | Times Cited: 4
AMPFLDAP: Adaptive Message Passing and Feature Fusion on Heterogeneous Network for LncRNA-Disease Associations Prediction
Yansen Su, Jingjing Liu, Qing-Wen Wu, et al.
Interdisciplinary Sciences Computational Life Sciences (2024) Vol. 16, Iss. 3, pp. 608-622
Closed Access
iGATTLDA: Integrative graph attention and transformer‐based model for predicting lncRNA‐Disease associations
Biffon Manyura Momanyi, Sebu Aboma Temesgen, Tianyu Wang, et al.
IET Systems Biology (2024)
Open Access
GRL-PUL: Predicting microbe-drug association based on graph representation learning and positive unlabeled learning
Junwu Liang, Yuping Sun, Jie Ling
Molecular Omics (2024) Vol. 21, Iss. 1, pp. 38-50
Closed Access
Regulatory association prediction of microRNA and cancer radiation response based on Bayesian personalized ranking matrix factorization and deep auto-encoder
Jing Li, Jie Liu, Xiaofeng Zheng, et al.
(2023), pp. 392-397
Closed Access
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