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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:

LPI-CNNCP: Prediction of lncRNA-protein interactions by using convolutional neural network with the copy-padding trick
Shao‐Wu Zhang, Xixi Zhang, Xiao-Nan Fan, et al.
Analytical Biochemistry (2020) Vol. 601, pp. 113767-113767
Closed Access | Times Cited: 40

Showing 1-25 of 40 citing articles:

Long non-coding RNA and RNA-binding protein interactions in cancer: Experimental and machine learning approaches
Hibah Shaath, Radhakrishnan Vishnubalaji, Ramesh Elango, et al.
Seminars in Cancer Biology (2022) Vol. 86, pp. 325-345
Open Access | Times Cited: 95
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: 44
Fusion of multi-source relationships and topology to infer lncRNA-protein interactions
Xinyu Zhang, Mingzhe Liu, Zhen Li, et al.
Molecular Therapy — Nucleic Acids (2024) Vol. 35, Iss. 2, pp. 102187-102187
Open Access | Times Cited: 13
Cross-domain contrastive graph neural network for lncRNA–protein interaction prediction
Hui Li, Bin Wu, Miaomiao Sun, et al.
Knowledge-Based Systems (2024) Vol. 296, pp. 111901-111901
Closed Access | Times Cited: 6
Finding lncRNA-Protein Interactions Based on Deep Learning With Dual-Net Neural Architecture
Lihong Peng, Chang Wang, Xiongfei Tian, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2021) Vol. 19, Iss. 6, pp. 3456-3468
Closed Access | Times Cited: 41
Capsule-LPI: a LncRNA–protein interaction predicting tool based on a capsule network
Ying Li, Hang Sun, Shiyao Feng, et al.
BMC Bioinformatics (2021) Vol. 22, Iss. 1
Open Access | Times Cited: 34
RPI-MDLStack: Predicting RNA–protein interactions through deep learning with stacking strategy and LASSO
Bin Yu, Xue Wang, Yaqun Zhang, et al.
Applied Soft Computing (2022) Vol. 120, pp. 108676-108676
Closed Access | Times Cited: 27
RPI-CapsuleGAN: Predicting RNA-protein interactions through an interpretable generative adversarial capsule network
Yifei Wang, Xue Wang, Cheng Chen, et al.
Pattern Recognition (2023) Vol. 141, pp. 109626-109626
Closed Access | Times Cited: 13
EDLMFC: an ensemble deep learning framework with multi-scale features combination for ncRNA–protein interaction prediction
Jingjing Wang, Yanpeng Zhao, Weikang Gong, et al.
BMC Bioinformatics (2021) Vol. 22, Iss. 1
Open Access | Times Cited: 29
An ensemble learning method combined with multiple feature representation strategies to predict lncRNA subcellular localizations
Lina Zhang, Sizan Gao, Quan Yuan, et al.
Computational Biology and Chemistry (2025), pp. 108336-108336
Closed Access
NPI-HGNN: A Heterogeneous Graph Neural Network-Based Approach for Predicting ncRNA-Protein Interactions
Xin Zhang, He Ma, Sizhe Wang, et al.
Interdisciplinary Sciences Computational Life Sciences (2025)
Closed Access
Prediction of enhancer–promoter interactions using the cross-cell type information and domain adversarial neural network
Fang Jing, Shao‐Wu Zhang, Shihua Zhang
BMC Bioinformatics (2020) Vol. 21, Iss. 1
Open Access | Times Cited: 28
ncRPI-LGAT: Prediction of ncRNA-protein interactions with line graph attention network framework
Yong Han, Shao‐Wu Zhang
Computational and Structural Biotechnology Journal (2023) Vol. 21, pp. 2286-2295
Open Access | Times Cited: 10
Attention-augmented multi-domain cooperative graph representation learning for molecular interaction prediction
Zhaowei Wang, Jun Meng, Haibin Li, et al.
Neural Networks (2025) Vol. 186, pp. 107265-107265
Closed Access
Negative sampling strategies impact the prediction of scale-free biomolecular network interactions with machine learning
Pengpai Li, Bowen Shao, Guoqing Zhao, et al.
BMC Biology (2025) Vol. 23, Iss. 1
Open Access
Recent Advances in Predicting Protein-lncRNA Interactions Using Machine Learning Methods
Pu-Feng Du, Han Yu, Zi-Ang Shen, et al.
Current Gene Therapy (2021) Vol. 22, Iss. 3, pp. 228-244
Closed Access | Times Cited: 22
LPI-HyADBS: a hybrid framework for lncRNA-protein interaction prediction integrating feature selection and classification
Liqian Zhou, Qi Duan, Xiongfei Tian, et al.
BMC Bioinformatics (2021) Vol. 22, Iss. 1
Open Access | Times Cited: 19
RPI-EDLCN: An Ensemble Deep Learning Framework Based on Capsule Network for ncRNA–Protein Interaction Prediction
Xiaoyi Li, Wenyan Qu, Jing Yan, et al.
Journal of Chemical Information and Modeling (2023) Vol. 64, Iss. 7, pp. 2221-2235
Closed Access | Times Cited: 7
Accurate prediction of protein-ATP binding residues using position-specific frequency matrix
Jun Hu, Linlin Zheng, Yansong Bai, et al.
Analytical Biochemistry (2021) Vol. 626, pp. 114241-114241
Closed Access | Times Cited: 16
Opportunities and Challenges of Predictive Approaches for the Non-coding RNA in Plants
Dong Xu, Wenya Yuan, Chunjie Fan, et al.
Frontiers in Plant Science (2022) Vol. 13
Open Access | Times Cited: 12
A Survey of Current Resources to Study lncRNA-Protein Interactions
Melcy Philip, Tyrone Chen, Sonika Tyagi
Non-Coding RNA (2021) Vol. 7, Iss. 2, pp. 33-33
Open Access | Times Cited: 15
NPI-RGCNAE: Fast Predicting ncRNA-Protein Interactions Using the Relational Graph Convolutional Network Auto-Encoder
Han Yu, Zi-Ang Shen, Pu-Feng Du
IEEE Journal of Biomedical and Health Informatics (2021) Vol. 26, Iss. 4, pp. 1861-1871
Closed Access | Times Cited: 11
An exhaustive review of computational prediction techniques for PPI sites, protein locations, and protein functions
P Bhat, Nagamma Patil
Network Modeling Analysis in Health Informatics and Bioinformatics (2023) Vol. 12, Iss. 1
Closed Access | Times Cited: 4
A comprehensive survey on deep learning-based identification and predicting the interaction mechanism of long non-coding RNAs
Biyu Diao, Jin Luo, Yu Guo
Briefings in Functional Genomics (2024) Vol. 23, Iss. 4, pp. 314-324
Closed Access | Times Cited: 1
Artificial intelligence methods enhance the discovery of RNA interactions
Gerardo Pepe, R Appierdo, Chiara Carrino, et al.
Frontiers in Molecular Biosciences (2022) Vol. 9
Open Access | Times Cited: 7
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