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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 26-50 of 288 citing articles:

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: 40
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: 40
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: 39
scDFC: A deep fusion clustering method for single-cell RNA-seq data
Dayu Hu, Ke Liang, Sihang Zhou, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 4
Closed Access | Times Cited: 39
Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA
Zhuohan Yu, Yanchi Su, Yifu Lu, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 36
Applications of deep learning in understanding gene regulation
Zhongxiao Li, Elva Gao, Juexiao Zhou, et al.
Cell Reports Methods (2023) Vol. 3, Iss. 1, pp. 100384-100384
Open Access | Times Cited: 29
scMAE: a masked autoencoder for single-cell RNA-seq clustering
Zhaoyu Fang, Ruiqing Zheng, Min Li
Bioinformatics (2024) Vol. 40, Iss. 1
Open Access | Times Cited: 13
DANCE: a deep learning library and benchmark platform for single-cell analysis
Jiayuan Ding, Renming Liu, Hongzhi Wen, et al.
Genome biology (2024) Vol. 25, Iss. 1
Open Access | Times Cited: 9
scMGATGRN: a multiview graph attention network–based method for inferring gene regulatory networks from single-cell transcriptomic data
Lin Yuan, Ling Zhao, Yufeng Jiang, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 6
Open Access | Times Cited: 8
Augmenting the human interactome for disease prediction through gene networks inferred from human cell atlas
Euijeong Sung, Junha Cha, Seungbyn Baek, et al.
Animal Cells and Systems (2025) Vol. 29, Iss. 1, pp. 11-20
Open Access | Times Cited: 1
A topology-preserving dimensionality reduction method for single-cell RNA-seq data using graph autoencoder
Zixiang Luo, Chenyu Xu, Zhen Zhang, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 53
Deep learning tackles single-cell analysis—a survey of deep learning for scRNA-seq analysis
Mario Flores, Zhentao Liu, Tinghe Zhang, et al.
Briefings in Bioinformatics (2021) Vol. 23, Iss. 1
Open Access | Times Cited: 46
Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning
Yuzhou Chang, Fei He, Juexin Wang, et al.
Computational and Structural Biotechnology Journal (2022) Vol. 20, pp. 4600-4617
Open Access | Times Cited: 37
scDCCA: deep contrastive clustering for single-cell RNA-seq data based on auto-encoder network
Jing Wang, Junfeng Xia, Haiyun Wang, et al.
Briefings in Bioinformatics (2022) Vol. 24, Iss. 1
Closed Access | Times Cited: 33
scIMC: a platform for benchmarking comparison and visualization analysis of scRNA-seq data imputation methods
Chichi Dai, Yi Jiang, Chenglin Yin, et al.
Nucleic Acids Research (2022) Vol. 50, Iss. 9, pp. 4877-4899
Open Access | Times Cited: 31
scGCL: an imputation method for scRNA-seq data based on graph contrastive learning
Zehao Xiong, Jiawei Luo, Wanwan Shi, et al.
Bioinformatics (2023) Vol. 39, Iss. 3
Open Access | Times Cited: 22
scRNA‐seq data analysis method to improve analysis performance
Junru Lu, Yuqi Sheng, Weiheng Qian, et al.
IET Nanobiotechnology (2023) Vol. 17, Iss. 3, pp. 246-256
Open Access | Times Cited: 21
Dimension-agnostic and granularity-based spatially variable gene identification using BSP
Juexin Wang, Jinpu Li, Skyler T. Kramer, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 20
Single-Cell Multiomics
Emily Flynn, Ana Almonte-Loya, Gabriela K. Fragiadakis
Annual Review of Biomedical Data Science (2023) Vol. 6, Iss. 1, pp. 313-337
Open Access | Times Cited: 19
Computational methods and challenges in analyzing intratumoral microbiome data
Qi Wang, Zhaoqian Liu, Anjun Ma, et al.
Trends in Microbiology (2023) Vol. 31, Iss. 7, pp. 707-722
Open Access | Times Cited: 18
Large-scale correlation network construction for unraveling the coordination of complex biological systems
Martin Becker, Huda Nassar, Camilo Espinosa, et al.
Nature Computational Science (2023) Vol. 3, Iss. 4, pp. 346-359
Open Access | Times Cited: 18
Deep single-cell RNA-seq data clustering with graph prototypical contrastive learning
Junseok Lee, Sungwon Kim, Dongmin Hyun, et al.
Bioinformatics (2023) Vol. 39, Iss. 6
Open Access | Times Cited: 17
Deep Learning in Single-cell Analysis
Dylan Molho, Jiayuan Ding, Wenzhuo Tang, et al.
ACM Transactions on Intelligent Systems and Technology (2024) Vol. 15, Iss. 3, pp. 1-62
Open Access | Times Cited: 7
Graph neural network approaches for single-cell data: a recent overview
Konstantinos Lazaros, Dimitrios E. Koumadorakis, Panagiotis Vlamos, et al.
Neural Computing and Applications (2024) Vol. 36, Iss. 17, pp. 9963-9987
Closed Access | Times Cited: 7
Integration tools for scRNA-seq data and spatial transcriptomics sequencing data
Chaorui Yan, Yanxu Zhu, Miao Chen, et al.
Briefings in Functional Genomics (2024) Vol. 23, Iss. 4, pp. 295-302
Closed Access | Times Cited: 6

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