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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:

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

Showing 1-25 of 46 citing articles:

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
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
OmicVerse: a framework for bridging and deepening insights across bulk and single-cell sequencing
Zehua Zeng, Yuqing Ma, Lei Hu, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 14
K-nearest-neighbors induced topological PCA for single cell RNA-sequence data analysis
Sean Cottrell, Yuta Hozumi, Guo‐Wei Wei
Computers in Biology and Medicine (2024) Vol. 175, pp. 108497-108497
Open Access | Times Cited: 8
Deep learning models for disease-associated circRNA prediction: a review
Yaojia Chen, Jiacheng Wang, Chuyu Wang, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 6
Closed Access | Times Cited: 36
Self-supervised deep clustering of single-cell RNA-seq data to hierarchically detect rare cell populations
Tianyuan Lei, Ruoyu Chen, Shaoqiang Zhang, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 6
Open Access | Times Cited: 17
xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data
Jing Gong, Minsheng Hao, Xin Zeng, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 16
A novel batch-effect correction method for scRNA-seq data based on Adversarial Information Factorization
Lily Monnier, Paul-Henry Cournède
PLoS Computational Biology (2024) Vol. 20, Iss. 2, pp. e1011880-e1011880
Open Access | Times Cited: 5
Deep Learning in Barcode Recognition: A Systematic Literature Review
Ratapol Wudhikarn, Phasit Charoenkwan, Kanokwan Malang
IEEE Access (2022) Vol. 10, pp. 8049-8072
Open Access | Times Cited: 19
transCAE: Enhancing cell type annotation in single-cell RNA-seq data with transfer learning and convolutional autoencoder
Qingchun Liu, Yan Xu
Journal of Molecular Biology (2025), pp. 168936-168936
Closed Access
Artificial Intelligence Methods in Infection Biology Research
Jacob Marcel Anter, Artur Yakimovich
Methods in molecular biology (2025), pp. 291-333
Closed Access
Dental Composite Performance Prediction Using Artificial Intelligence
Karla Paniagua, Kyumin Whang, Kamal Kant Joshi, et al.
Journal of Dental Research (2025)
Open Access
A novel coarsened graph learning method for scalable single-cell data analysis
M. C. Kataria, Ekta Srivastava, Arjun Kumar, et al.
Computers in Biology and Medicine (2025) Vol. 188, pp. 109873-109873
Closed Access
scFTAT: a novel cell annotation method integrating FFT and transformer
Binhua Tang, Y. Chen
BMC Bioinformatics (2025) Vol. 26, Iss. 1
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
Dissection of Tumoral Niches Using Spatial Transcriptomics and Deep Learning
Karla Paniagua, Yufang Jin, Yidong Chen, et al.
iScience (2025) Vol. 28, Iss. 4, pp. 112214-112214
Open Access
scVAEDer: integrating deep diffusion models and variational autoencoders for single-cell transcriptomics analysis
Mehrshad Sadria, Anita T. Layton
Genome biology (2025) Vol. 26, Iss. 1
Open Access
OmicVerse: A single pipeline for exploring the entire transcriptome universe
Zehua Zeng, Yuqing Ma, Lei Hu, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 9
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
The Power of Two: integrating deep diffusion models and variational autoencoders for single-cell transcriptomics analysis
Mehrshad Sadria, Anita T. Layton
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 8
Single-Cell Analysis in the Omics Era: Technologies and Applications in Cancer
Michele Massimino, Federica Martorana, Stefania Stella, et al.
Genes (2023) Vol. 14, Iss. 7, pp. 1330-1330
Open Access | Times Cited: 8
Denoising adaptive deep clustering with self-attention mechanism on single-cell sequencing data
Yansen Su, Rongxin Lin, Jing Wang, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 2
Closed Access | Times Cited: 6
scFseCluster: a feature selection-enhanced clustering for single-cell RNA-seq data
Zongqin Wang, Xiaojun Xie, Shouyang Liu, et al.
Life Science Alliance (2023) Vol. 6, Iss. 12, pp. e202302103-e202302103
Open Access | Times Cited: 4
A comprehensive survey on deep learning-based identification and predicting the interaction mechanism of long non-coding RNAs
Biyu Diao, Jin Luo, Yu Guo
Briefings in Functional Genomics (2024) Vol. 23, Iss. 4, pp. 314-324
Closed Access | Times Cited: 1
TransC-ac4C: Identification of N4-acetylcytidine (ac4C) sites in mRNA using deep learning
Dian Liu, Zi Liu, Yunpeng Xia, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2024) Vol. 21, Iss. 5, pp. 1403-1412
Closed Access | Times Cited: 1
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