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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:

Hypergraph-based logistic matrix factorization for metabolite–disease interaction prediction
Yingjun Ma, Yuanyuan Ma
Bioinformatics (2021) Vol. 38, Iss. 2, pp. 435-443
Closed Access | Times Cited: 23

Showing 23 citing articles:

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: 194
Pre-training graph neural networks for link prediction in biomedical networks
Yahui Long, Min Wu, Yong Liu, et al.
Bioinformatics (2022) Vol. 38, Iss. 8, pp. 2254-2262
Open Access | Times Cited: 54
DeepMNE: Deep Multi-Network Embedding for lncRNA-Disease Association Prediction
Yingjun Ma
IEEE Journal of Biomedical and Health Informatics (2022) Vol. 26, Iss. 7, pp. 3539-3549
Closed Access | Times Cited: 24
Generalized matrix factorization based on weighted hypergraph learning for microbe-drug association prediction
Yingjun Ma, Qingquan Liu
Computers in Biology and Medicine (2022) Vol. 145, pp. 105503-105503
Closed Access | Times Cited: 20
Hyperbolic matrix factorization improves prediction of drug-target associations
Aleksandar Poleksić
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 11
RGCGT: A high-order feature learning framework for predicting disease-metabolite interaction using residual graph convolution and graph transformer
W.D. Liu, Pengli Lu, Jinai Gao
Expert Systems with Applications (2025), pp. 126637-126637
Closed Access
Kernel Bayesian nonlinear matrix factorization based on variational inference for human–virus protein–protein interaction prediction
Yingjun Ma, Yongbiao Zhao, Yuanyuan Ma
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 3
KDGene: knowledge graph completion for disease gene prediction using interactional tensor decomposition
Xinyan Wang, Kuo Yang, Ting Jia, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 3
Open Access | Times Cited: 2
Prediction of disease-related miRNAs by voting with multiple classifiers
Changlong Gu, M Kellis
BMC Bioinformatics (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 6
Weighted hypergraph learning and adaptive inductive matrix completion for SARS-CoV-2 drug repositioning
Yingjun Ma, Junjiang Zhong, Nenghui Zhu
Methods (2023) Vol. 219, pp. 102-110
Open Access | Times Cited: 4
MGDHGS: Gene-bridged metabolite-disease relationships prediction via GraphSAGE and self-attention mechanism
Pengli Lu, Ling Li
Computational Biology and Chemistry (2024) Vol. 109, pp. 108036-108036
Closed Access | Times Cited: 1
EEG-based patient-specific seizure prediction based on Spatial–Temporal Hypergraph Attention Transformer
Changxu Dong, Dengdi Sun, Zejing Zhang, et al.
Biomedical Signal Processing and Control (2024) Vol. 100, pp. 107075-107075
Closed Access | Times Cited: 1
Logistic tensor decomposition with sparse subspace learning for prediction of multiple disease types of human–virus protein–protein interactions
Yingjun Ma, Junjiang Zhong
Briefings in Bioinformatics (2022) Vol. 24, Iss. 1
Closed Access | Times Cited: 5
Kernel Bayesian logistic tensor decomposition with automatic rank determination for predicting multiple types of miRNA-disease associations
Yingjun Ma, Yuanyuan Ma
PLoS Computational Biology (2024) Vol. 20, Iss. 7, pp. e1012287-e1012287
Open Access
A Graph Attention Network Combining Community Detection and Natural Language Processing to Predict Metabolite-Disease Associations
Jiejun Zhou
(2024), pp. 233-237
Closed Access
HEnsem_DTIs: A heterogeneous ensemble learning model for drug-target interactions prediction
Mohammad Reza Keyvanpour, Yasaman Asghari, Soheila Mehrmolaei
Chemometrics and Intelligent Laboratory Systems (2024) Vol. 253, pp. 105224-105224
Closed Access
MGDRGCN: A novel framework for predicting metabolite-disease connections using tripartite network and relational graph convolutional network
Pengli Lu, Ling Li
Journal of Computational Science (2024), pp. 102477-102477
Closed Access
DMNAG: Prediction of disease-metabolite associations based on Neighborhood Aggregation Graph Transformer
Pengli Lu, Jinai Gao, W.D. Liu
Computational Biology and Chemistry (2024) Vol. 115, pp. 108320-108320
Closed Access
A new method for accelerating disease metabolite prediction based on NPU
Haocheng Li, J. Li
(2024), pp. 280-288
Closed Access
Efficient Computation of Hyper-Triangles on Hypergraphs
Haozhe Yin, Kai Wang, Wenjie Zhang, et al.
Proceedings of the VLDB Endowment (2024) Vol. 18, Iss. 3, pp. 729-742
Closed Access
Deep learning-driven automatic reconstruction of genome-scale metabolic networks
Xiaoyi Liu, Hongpeng Yang, Chengwei Ai, et al.
Research Square (Research Square) (2023)
Open Access | Times Cited: 1
A hypernetwork-based urn model for explaining collective dynamics
Jiali Lu, Haifeng Du, Xiaochen He
PLoS ONE (2023) Vol. 18, Iss. 9, pp. e0291778-e0291778
Open Access | Times Cited: 1
MultiDS-MDA: Integrating multiple data sources into heterogeneous network for predicting novel metabolite-drug associations
Xiuhong Li, Hao Yuan, Xiaoliang Wu, et al.
Computers in Biology and Medicine (2023) Vol. 162, pp. 107067-107067
Closed Access
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