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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-BLS: Predicting lncRNA–protein interactions with a broad learning system-based stacked ensemble classifier
Xiao-Nan Fan, Shao‐Wu Zhang
Neurocomputing (2019) Vol. 370, pp. 88-93
Closed Access | Times Cited: 69

Showing 1-25 of 69 citing articles:

Research Review for Broad Learning System: Algorithms, Theory, and Applications
Xinrong Gong, Tong Zhang, C. L. Philip Chen, et al.
IEEE Transactions on Cybernetics (2021) Vol. 52, Iss. 9, pp. 8922-8950
Closed Access | Times Cited: 205
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
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: 63
DPDDI: a deep predictor for drug-drug interactions
Jian Feng, Shao‐Wu Zhang, Jian‐Yu Shi
BMC Bioinformatics (2020) Vol. 21, Iss. 1
Open Access | Times Cited: 125
NPI-GNN: Predicting ncRNA–protein interactions with deep graph neural networks
Zi-Ang Shen, Tao Luo, Yuan-Ke Zhou, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 5
Closed Access | Times Cited: 59
Digital twins model and its updating method for heating, ventilation and air conditioning system using broad learning system algorithm
Kang Chen, Xu Zhu, Burkay Anduv, et al.
Energy (2022) Vol. 251, pp. 124040-124040
Closed Access | Times Cited: 54
EnANNDeep: An Ensemble-based lncRNA–protein Interaction Prediction Framework with Adaptive k-Nearest Neighbor Classifier and Deep Models
Lihong Peng, Jingwei Tan, Xiongfei Tian, et al.
Interdisciplinary Sciences Computational Life Sciences (2022) Vol. 14, Iss. 1, pp. 209-232
Closed Access | Times Cited: 36
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
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
LPI-deepGBDT: a multiple-layer deep framework based on gradient boosting decision trees for lncRNA–protein interaction identification
Liqian Zhou, Zhao Wang, Xiongfei Tian, et al.
BMC Bioinformatics (2021) Vol. 22, Iss. 1
Open Access | Times Cited: 40
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
HeadTailTransfer: An efficient sampling method to improve the performance of graph neural network method in predicting sparse ncRNA–protein interactions
Jinhang Wei, Linlin Zhuo, Shiyao Pan, et al.
Computers in Biology and Medicine (2023) Vol. 157, pp. 106783-106783
Closed Access | Times Cited: 15
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
Broad stochastic configuration network for regression
Chenglong Zhang, Shifei Ding, Wei Du
Knowledge-Based Systems (2022) Vol. 243, pp. 108403-108403
Closed Access | Times Cited: 21
SE-BLS: A Shapley-Value-Based Ensemble Broad Learning System with collaboration-based feature selection and CAM visualization
Jianguo Miao, Xuanxuan Liu, Li Guo, et al.
Knowledge-Based Systems (2024) Vol. 301, pp. 112343-112343
Closed Access | Times Cited: 4
RNA-protein interaction prediction using network-guided deep learning
Haoquan Liu, Yiren Jian, Chen Zeng, et al.
Communications Biology (2025) Vol. 8, Iss. 1
Open 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
SeqMG-RPI: A Sequence-Based Framework Integrating Multi-Scale RNA Features and Protein Graphs for RNA-Protein Interaction Prediction
Teng Ma, Mingjian Jiang, Shunpeng Pang, et al.
Journal of Chemical Information and Modeling (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
Broad learning systems: An overview of recent advances, applications, challenges and future directions
Yonghe Chu, Yan Guo, Weiping Ding, et al.
Neurocomputing (2025), pp. 130337-130337
Closed Access
DeepLPI: a multimodal deep learning method for predicting the interactions between lncRNAs and protein isoforms
Dipan Shaw, Hao Chen, Minzhu Xie, et al.
BMC Bioinformatics (2021) Vol. 22, Iss. 1
Open Access | Times Cited: 23
A novel lncRNA–protein interaction prediction method based on deep forest with cascade forest structure
Xiongfei Tian, Ling Shen, Zhenwu Wang, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 23
LPI-EnEDT: an ensemble framework with extra tree and decision tree classifiers for imbalanced lncRNA-protein interaction data classification
Lihong Peng, Ruya Yuan, Ling Shen, et al.
BioData Mining (2021) Vol. 14, Iss. 1
Open Access | Times Cited: 23
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