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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:

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

Showing 1-25 of 56 citing articles:

Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis
Yanshuo Chen, Yixuan Wang, Yuelong Chen, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 52
Identifying spatial domain by adapting transcriptomics with histology through contrastive learning
Yuansong Zeng, Rui Yin, Mai Luo, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 2
Open Access | Times Cited: 28
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
Single-cell omics: experimental workflow, data analyses and applications
Fengying Sun, Haoyan Li, Dongqing Sun, et al.
Science China Life Sciences (2024)
Closed Access | Times Cited: 10
Deep learning and ensemble deep learning for circRNA-RBP interaction prediction in the last decade: A review
Dilan Lasantha, Sugandima Vidanagamachchi, Sam Nallaperuma
Engineering Applications of Artificial Intelligence (2023) Vol. 123, pp. 106352-106352
Closed Access | Times Cited: 19
Predicting RNA structures and functions by artificial intelligence
Jun Zhang, Mei Lang, Yaoqi Zhou, et al.
Trends in Genetics (2023) Vol. 40, Iss. 1, pp. 94-107
Closed Access | Times Cited: 18
scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data
Ziqi Zhang, Xinye Zhao, Mehak Bindra, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 7
Emerging artificial intelligence applications in Spatial Transcriptomics analysis
Yijun Li, Stefan Stanojevic, Lana X. Garmire
Computational and Structural Biotechnology Journal (2022) Vol. 20, pp. 2895-2908
Open Access | Times Cited: 26
Procrustes is a machine-learning approach that removes cross-platform batch effects from clinical RNA sequencing data
Nikita Kotlov, Kirill Shaposhnikov, Cagdas Tazearslan, et al.
Communications Biology (2024) Vol. 7, Iss. 1
Open Access | Times Cited: 5
Benchmarking cross-species single-cell RNA-seq data integration methods: towards a cell type tree of life
Zhong Hua-wen, Wenkai Han, David Gómez-Cabrero, et al.
Nucleic Acids Research (2025) Vol. 53, Iss. 1
Open Access
Integration of unpaired single cell omics data by deep transfer graph convolutional network
Yulong Kan, Y. Qi, Zhongxiao Zhang, et al.
PLoS Computational Biology (2025) Vol. 21, Iss. 1, pp. e1012625-e1012625
Open Access
A generative deep neural network for pan-digestive tract cancer survival analysis
Lekai Xu, Tianjun Lan, Yiqian Huang, et al.
BioData Mining (2025) Vol. 18, Iss. 1
Open Access
HEDDI-Net: heterogeneous network embedding for drug-disease association prediction and drug repurposing, with application to Alzheimer’s disease
Yin-Yuan Su, Hsuan‐Cheng Huang, Yu‐Ting Lin, et al.
Journal of Translational Medicine (2025) Vol. 23, Iss. 1
Open Access
scSCC: A swapped contrastive learning‐based clustering method for single‐cell gene expression data
Xiang Wang, Sansheng Yang, Hongwei Li
Quantitative Biology (2025) Vol. 13, Iss. 2
Open Access
A comparison of integration methods for single‐cell RNA sequencing data and ATAC sequencing data
Yulong Kan, Weihao Wang, Y. Qi, et al.
Quantitative Biology (2025) Vol. 13, Iss. 2
Open Access
A scalable sparse neural network framework for rare cell type annotation of single-cell transcriptome data
Yuqi Cheng, Xingyu Fan, Jianing Zhang, et al.
Communications Biology (2023) Vol. 6, Iss. 1
Open Access | Times Cited: 10
Integration of scRNA-seq data by disentangled representation learning with condition domain adaptation
Renjing Liu, Kun Qian, Xinwei He, et al.
BMC Bioinformatics (2024) Vol. 25, Iss. 1
Open Access | Times Cited: 3
Delineating the effective use of self-supervised learning in single-cell genomics
Till Richter, Mojtaba Bahrami, Yufan Xia, et al.
Nature Machine Intelligence (2024)
Open Access | Times Cited: 3
Con-AAE: contrastive cycle adversarial autoencoders for single-cell multi-omics alignment and integration
Xuesong Wang, Zhihang Hu, Tingyang Yu, et al.
Bioinformatics (2023) Vol. 39, Iss. 4
Open Access | Times Cited: 8
Dual-GCN-based deep clustering with triplet contrast for ScRNA-seq data analysis
Linjie Wang, Wei Li, Weidong Xie, et al.
Computational Biology and Chemistry (2023) Vol. 106, pp. 107924-107924
Closed Access | Times Cited: 7
scCRT: a contrastive-based dimensionality reduction model for scRNA-seq trajectory inference
Yuchen Shi, Jian Wan, Xin Zhang, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 3
Open Access | Times Cited: 2
GLOBE: a contrastive learning-based framework for integrating single-cell transcriptome datasets
Xuhua Yan, Ruiqing Zheng, Min Li
Briefings in Bioinformatics (2022) Vol. 23, Iss. 5
Closed Access | Times Cited: 12
Accurate cell type annotation for single‐cell chromatin accessibility data via contrastive learning and reference guidance
Siyu Li, Songming Tang, Yunchang Wang, et al.
Quantitative Biology (2024) Vol. 12, Iss. 1, pp. 85-99
Open Access | Times Cited: 2
Delineating the Effective Use of Self-Supervised Learning in Single-Cell Genomics
Till Richter, Mojtaba Bahrami, Yufan Xia, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 2
Thinking points for effective batch correction on biomedical data
Harvard Wai Hann Hui, Weijia Kong, Wilson Wen Bin Goh
Briefings in Bioinformatics (2024) Vol. 25, Iss. 6
Open Access | Times Cited: 2
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