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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:

Predicting miRNA-disease association via graph attention learning and multiplex adaptive modality fusion
Z M Jin, Minhui Wang, Chang Tang, et al.
Computers in Biology and Medicine (2023) Vol. 169, pp. 107904-107904
Closed Access | Times Cited: 13

Showing 13 citing articles:

Predicting noncoding RNA and disease associations using multigraph contrastive learning
Si-Lin Sun, Yi Jiang, Jun-Ping Yang, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 1
Graph Convolutional Network with Neural Collaborative Filtering for Predicting miRNA-Disease Association
Jihwan Ha
Biomedicines (2025) Vol. 13, Iss. 1, pp. 136-136
Open Access | Times Cited: 1
Subgraph Topology and Dynamic Graph Topology Enhanced Graph Learning and Pairwise Feature Context Relationship Integration for Predicting Disease-Related miRNAs
Ping Xuan, Xiaoying Qi, Sentao Chen, et al.
Journal of Chemical Information and Modeling (2025)
Closed Access
DeepWalk-Based Graph Embeddings for miRNA–Disease Association Prediction Using Deep Neural Network
Jihwan Ha
Biomedicines (2025) Vol. 13, Iss. 3, pp. 536-536
Open Access
Gene-related multi-network collaborative deep feature learning for predicting miRNA-disease associations
Pengli Lu, Xu Cao
Computers & Electrical Engineering (2025) Vol. 123, pp. 110242-110242
Closed Access
MUSCLE: multi-view and multi-scale attentional feature fusion for microRNA–disease associations prediction
Boya Ji, Haitao Zou, Li‐Wen Xu, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 3
Open Access | Times Cited: 3
DCSGMDA: A dual-channel convolutional model based on stacked deep learning collaborative gradient decomposition for predicting miRNA-disease associations
Xu Cao, Pengli Lu
Computational Biology and Chemistry (2024) Vol. 113, pp. 108201-108201
Closed Access | Times Cited: 1
MNESEDA: A prior-guided subgraph representation learning framework for predicting disease-related enhancers
Jinsheng Xu, Weicheng Sun, Kai Li, et al.
Knowledge-Based Systems (2024) Vol. 294, pp. 111734-111734
Closed Access
A Deep Metric Learning Based Method for Predicting MiRNA-Disease Associations
Nguyen-Phuc-Xuan Quynh, Hoai-Nhan Tran, Cheng Yan, et al.
Lecture notes in computer science (2024), pp. 262-273
Closed Access
A method for miRNA diffusion association prediction using machine learning decoding of multi-level heterogeneous graph Transformer encoded representations
SiJian Wen, YinBo Liu, Guang Yang, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access
MiRNA-Disease Associations Prediction Based on Improving Feature Vectors Quality Combined with Highly Reliable Negative Samples Selection
Nguyen Phuc Xuan Quynh, Tran Hoai Nhan, Lê Anh Phương
Lecture notes in networks and systems (2024), pp. 3-15
Closed Access
MCAGU-Net: A model for composite fault diagnosis of multi-sensor node networks
Kangshuai Zhang, Quancheng Zhang, Qi Liu, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 141, pp. 109814-109814
Closed Access
Predicting microbe-disease associations via graph neural network and contrastive learning
Cong Jiang, J. H. Feng, Boxuan Shan, et al.
Frontiers in Microbiology (2024) Vol. 15
Open Access
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