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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: 270

Showing 1-25 of 270 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: 1156
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: 199
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: 153
Graph representation learning in biomedicine and healthcare
Michelle M. Li, Kexin Huang, Marinka Żitnik
Nature Biomedical Engineering (2022) Vol. 6, Iss. 12, pp. 1353-1369
Open Access | Times Cited: 144
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: 108
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: 101
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: 87
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: 84
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: 80
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: 70
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: 66
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: 62
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: 37
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: 20
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: 18
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: 106
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: 56
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: 52
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: 50
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: 50
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: 40
Cell graph neural networks enable the precise prediction of patient survival in gastric cancer
Yanan Wang, Yu Guang Wang, Changyuan Hu, et al.
npj Precision Oncology (2022) Vol. 6, Iss. 1
Open Access | Times Cited: 39
ZINB-Based Graph Embedding Autoencoder for Single-Cell RNA-Seq Interpretations
Zhuohan Yu, Yifu Lu, Yunhe Wang, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2022) Vol. 36, Iss. 4, pp. 4671-4679
Open Access | Times Cited: 38
Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning
Chunman Zuo, Yijian Zhang, Chen Cao, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 36
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