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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:

scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses
Juexin Wang, Anjun Ma, Yuzhou Chang, et al.
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 288

Showing 1-25 of 288 citing articles:

A guide to machine learning for biologists
Joe G. Greener, Shaun M. Kandathil, Lewis Moffat, et al.
Nature Reviews Molecular Cell Biology (2021) Vol. 23, Iss. 1, pp. 40-55
Open Access | Times Cited: 1250
Graph Neural Networks and Their Current Applications in Bioinformatics
Xiaomeng Zhang, Liang Li, Lin Liu, et al.
Frontiers in Genetics (2021) Vol. 12
Open Access | Times Cited: 215
Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine
Peng Zhang, Dingfan Zhang, Wuai Zhou, et al.
Briefings in Bioinformatics (2023) Vol. 25, Iss. 1
Open Access | Times Cited: 169
Graph Neural Networks in Network Neuroscience
Alaa Bessadok, Mohamed Ali Mahjoub, Islem Rekik
IEEE Transactions on Pattern Analysis and Machine Intelligence (2022) Vol. 45, Iss. 5, pp. 5833-5848
Open Access | Times Cited: 166
Graph representation learning in biomedicine and healthcare
Michelle M. Li, Kexin Huang, Marinka Žitnik
Nature Biomedical Engineering (2022) Vol. 6, Iss. 12, pp. 1353-1369
Open Access | Times Cited: 160
scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network
Xin Shao, Haihong Yang, Xiang Zhuang, et al.
Nucleic Acids Research (2021) Vol. 49, Iss. 21, pp. e122-e122
Open Access | Times Cited: 112
Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review
Fadi Alharbi, Aleksandar Vakanski
Bioengineering (2023) Vol. 10, Iss. 2, pp. 173-173
Open Access | Times Cited: 103
Deep transfer learning of cancer drug responses by integrating bulk and single-cell RNA-seq data
Junyi Chen, Xiaoying Wang, Anjun Ma, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 94
Single-cell biological network inference using a heterogeneous graph transformer
Anjun Ma, Xiaoying Wang, Jingxian Li, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 89
Neural relational inference to learn long-range allosteric interactions in proteins from molecular dynamics simulations
Jingxuan Zhu, Juexin Wang, Weiwei Han, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 78
Deep learning shapes single-cell data analysis
Qin Ma, Dong Xu
Nature Reviews Molecular Cell Biology (2022) Vol. 23, Iss. 5, pp. 303-304
Open Access | Times Cited: 74
Graph embedding and Gaussian mixture variational autoencoder network for end-to-end analysis of single-cell RNA sequencing data
Junlin Xu, Jielin Xu, Yajie Meng, et al.
Cell Reports Methods (2023) Vol. 3, Iss. 1, pp. 100382-100382
Open Access | Times Cited: 67
Benchmarking spatial clustering methods with spatially resolved transcriptomics data
Zhiyuan Yuan, Fangyuan Zhao, Senlin Lin, et al.
Nature Methods (2024) Vol. 21, Iss. 4, pp. 712-722
Closed Access | Times Cited: 52
Trends and Potential of Machine Learning and Deep Learning in Drug Study at Single-Cell Level
Ren Qi, Quan Zou
Research (2023) Vol. 6
Open Access | Times Cited: 41
Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges
Manoj Kumar Goshisht
ACS Omega (2024) Vol. 9, Iss. 9, pp. 9921-9945
Open Access | Times Cited: 27
Multimodal data integration for oncology in the era of deep neural networks: a review
Asim Waqas, Aakash Tripathi, Ravi P. Ramachandran, et al.
Frontiers in Artificial Intelligence (2024) Vol. 7
Open Access | Times Cited: 22
MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer
Xiaoying Wang, Maoteng Duan, Jingxian Li, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 16
scREAD: A Single-Cell RNA-Seq Database for Alzheimer's Disease
Jing Jiang, Cankun Wang, Ren Qi, et al.
iScience (2020) Vol. 23, Iss. 11, pp. 101769-101769
Open Access | Times Cited: 110
Highly Sensitive Tin‐Lead Perovskite Photodetectors with Over 450 Days Stability Enabled by Synergistic Engineering for Pulse Oximetry System
Lijuan He, Gangjian Hu, Jizhong Jiang, et al.
Advanced Materials (2022) Vol. 35, Iss. 10
Closed Access | Times Cited: 60
Self-supervised contrastive learning for integrative single cell RNA-seq data analysis
Wenkai Han, Yuqi Cheng, Jiayang Chen, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 5
Open Access | Times Cited: 56
Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network
Yanglan Gan, Xingyu Huang, Guobing Zou, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 2
Open Access | Times Cited: 55
scGAC: a graph attentional architecture for clustering single-cell RNA-seq data
Cheng Yi, Xiuli Ma
Bioinformatics (2022) Vol. 38, Iss. 8, pp. 2187-2193
Closed Access | Times Cited: 54
Graph attention network for link prediction of gene regulations from single-cell RNA-sequencing data
Guangyi Chen, Zhi‐Ping Liu
Bioinformatics (2022) Vol. 38, Iss. 19, pp. 4522-4529
Open Access | Times Cited: 48
Application of Deep Learning on Single-Cell RNA Sequencing Data Analysis: A Review
Matthew Brendel, Chang Su, Zilong Bai, et al.
Genomics Proteomics & Bioinformatics (2022) Vol. 20, Iss. 5, pp. 814-835
Open Access | Times Cited: 44
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