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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:

Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data
Yifan Zhao, Huiyu Cai, Zuobai Zhang, et al.
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 68

Showing 1-25 of 68 citing articles:

Biologically informed deep learning to query gene programs in single-cell atlases
Mohammad Lotfollahi, Sergei Rybakov, Karin Hrovatin, et al.
Nature Cell Biology (2023)
Open Access | Times Cited: 55
Drug target prediction through deep learning functional representation of gene signatures
Hao Chen, Frederick J. King, Bin Zhou, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 24
Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning
Andrea Riba, Attila Oravecz, Matej Durik, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 50
MIRA: joint regulatory modeling of multimodal expression and chromatin accessibility in single cells
Allen W. Lynch, Christina V. Theodoris, Henry W. Long, et al.
Nature Methods (2022) Vol. 19, Iss. 9, pp. 1097-1108
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: 43
siVAE: interpretable deep generative models for single-cell transcriptomes
Yongin Choi, Ruoxin Li, Gerald Quon
Genome biology (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 25
Artificial Intelligence in battling infectious diseases: A transformative role
Chunhui Li, Guoguo Ye, Yinghan Jiang, et al.
Journal of Medical Virology (2024) Vol. 96, Iss. 1
Closed Access | Times Cited: 8
Dissection and integration of bursty transcriptional dynamics for complex systems
Cheng Gao, Suriyanarayanan Vaikuntanathan, Samantha J. Riesenfeld
Proceedings of the National Academy of Sciences (2024) Vol. 121, Iss. 18
Open Access | Times Cited: 5
BuDDI: Bulk Deconvolution with Domain Invariance to predict cell-type-specific perturbations from bulk
Natalie R. Davidson, Fan Zhang, Casey S. Greene
PLoS Computational Biology (2025) Vol. 21, Iss. 1, pp. e1012742-e1012742
Open Access
An intelligent framework of heuristic approach-aided optimal gene selection and residual LSTM with MLP for disease prediction in rice crop using gene expression data
Vijaya Lakshmi Adluri, Raju Bhukya
Signal Image and Video Processing (2025) Vol. 19, Iss. 4
Closed Access
Hypergraph factorization for multi-tissue gene expression imputation
Ramón Viñas, Chaitanya K. Joshi, Dobrik Georgiev, et al.
Nature Machine Intelligence (2023) Vol. 5, Iss. 7, pp. 739-753
Open Access | Times Cited: 14
Species-agnostic transfer learning for cross-species transcriptomics data integration without gene orthology
Youngjun Park, Nils Muttray, Anne-Christin Hauschild
Briefings in Bioinformatics (2024) Vol. 25, Iss. 2
Open Access | Times Cited: 4
DeepQA: A Unified Transcriptome‐Based Aging Clock Using Deep Neural Networks
Hongqian Qi, Hongchen Zhao, Enyi Li, et al.
Aging Cell (2025)
Open Access
Artificial Intelligence-Assisted Multimode Microrobot Swarm Behaviors
Xuanjie Xia, Miao Ni, Mengchen Wang, et al.
ACS Nano (2025)
Closed Access
A Roadmap for a Consensus Human Skin Cell Atlas and Single-Cell Data Standardization
Axel A. Almet, Hao Yuan, Karl Annusver, et al.
Journal of Investigative Dermatology (2023) Vol. 143, Iss. 9, pp. 1667-1677
Open Access | Times Cited: 10
Supervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer’s disease progression
L. M. Hodgson, Yue Li, Yasser Iturria‐Medina, et al.
Communications Biology (2024) Vol. 7, Iss. 1
Open Access | Times Cited: 3
Interpretable deep learning in single-cell omics
Manoj M Wagle, Siqu Long, Carissa Chen, et al.
Bioinformatics (2024) Vol. 40, Iss. 6
Open Access | Times Cited: 3
Single-cell multi-omics topic embedding reveals cell-type-specific and COVID-19 severity-related immune signatures
Manqi Zhou, Hao Zhang, Zilong Bai, et al.
Cell Reports Methods (2023) Vol. 3, Iss. 8, pp. 100563-100563
Open Access | Times Cited: 7
Single-Cell Transcriptomic Analysis of Dental Pulp and Periodontal Ligament Stem Cells
Yuchen Yang, Tomaz Alves, Michael Z. Miao, et al.
Journal of Dental Research (2023) Vol. 103, Iss. 1, pp. 71-80
Closed Access | Times Cited: 7
scMGCN: A Multi-View Graph Convolutional Network for Cell Type Identification in scRNA-seq Data
Hongmin Sun, Haowen Qu, Kaifu Duan, et al.
International Journal of Molecular Sciences (2024) Vol. 25, Iss. 4, pp. 2234-2234
Open Access | Times Cited: 2
The effect of data transformation on low-dimensional integration of single-cell RNA-seq
Youngjun Park, Anne-Christin Hauschild
BMC Bioinformatics (2024) Vol. 25, Iss. 1
Open Access | Times Cited: 2
Deep Learning of radiology-genomics integration for computational oncology: A mini review
Feng-ao Wang, Y Li, Tao Zeng
Computational and Structural Biotechnology Journal (2024) Vol. 23, pp. 2708-2716
Open Access | Times Cited: 2
Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics
Axel A. Almet, Yuan‐Chen Tsai, Momoko Watanabe, et al.
Nature Methods (2024) Vol. 21, Iss. 10, pp. 1806-1817
Open Access | Times Cited: 2
Single-cell pair-wise relationships untangled by composite embedding model
Sishir Subedi, Yongjin Park
iScience (2023) Vol. 26, Iss. 2, pp. 106025-106025
Open Access | Times Cited: 6
CLAIRE: contrastive learning-based batch correction framework for better balance between batch mixing and preservation of cellular heterogeneity
Xuhua Yan, Ruiqing Zheng, Fang‐Xiang Wu, et al.
Bioinformatics (2023) Vol. 39, Iss. 3
Open Access | Times Cited: 6
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