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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 26-50 of 68 citing articles:

GTM-decon: guided-topic modeling of single-cell transcriptomes enables sub-cell-type and disease-subtype deconvolution of bulk transcriptomes
Lakshmipuram S. Swapna, Michael Huang, Yue Li
Genome biology (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 6
Biologically informed deep learning to infer gene program activity in single cells
Mohammad Lotfollahi, Sergei Rybakov, Karin Hrovatin, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2022)
Open Access | Times Cited: 10
Transfer learning compensates limited data, batch effects and technological heterogeneity in single-cell sequencing
Youngjun Park, Anne-Christin Hauschild, Dominik Heider
NAR Genomics and Bioinformatics (2021) Vol. 3, Iss. 4
Open Access | Times Cited: 12
Learning interpretable cellular embedding for inferring biological mechanisms underlying single-cell transcriptomics
Kang‐Lin Hsieh, Kai Zhang, Yan Chu, et al.
medRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 1
GFETM: Genome Foundation-Based Embedded Topic Model for scATAC-seq Modeling
Yimin Fan, Yu Li, Jun Ding, et al.
Lecture notes in computer science (2024), pp. 314-319
Closed Access | Times Cited: 1
scGADR: Dimensionality Reduction of Single-Cell RNA-seq Data with ZINB-Based Graph Attention Autoencoder
Yongxuan Tang, Jiawei Luo, XU Zhong-yuan, et al.
Lecture notes in computer science (2024), pp. 357-368
Closed Access | Times Cited: 1
Pseudo-grading of tumor subpopulations from single-cell transcriptomic data using Phenotype Algebra
N Bhattacharya, Anja Rockstroh, Sanket Suhas Deshpande, et al.
(2024)
Open Access | Times Cited: 1
Designing interpretable deep learning applications for functional genomics: a quantitative analysis
Arno van Hilten, Sonja Katz, Edoardo Saccenti, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 5
Open Access | Times Cited: 1
scMoE: single-cell mixture of experts for learning hierarchical, cell-type-specific, and interpretable representations from heterogeneous scRNA-seq data
Michael Huang, Yue Li
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 1
Unraveling dynamically encoded latent transcriptomic patterns in pancreatic cancer cells by topic modeling
Yichen Zhang, Mohammadali Khalilitousi, Yongjin Park
Cell Genomics (2023) Vol. 3, Iss. 9, pp. 100388-100388
Open Access | Times Cited: 4
Gene set proximity analysis: expanding gene set enrichment analysis through learned geometric embeddings, with drug-repurposing applications in COVID-19
Henry C. Cousins, Taryn O. Hall, Yinglong Guo, et al.
Bioinformatics (2022) Vol. 39, Iss. 1
Open Access | Times Cited: 7
Variational autoencoding of gene landscapes during mouse CNS development uncovers layered roles of Polycomb Repressor Complex 2
Ariane Mora, Jonathan Rakar, Ignacio Cobeta, et al.
Nucleic Acids Research (2022) Vol. 50, Iss. 3, pp. 1280-1296
Open Access | Times Cited: 6
Single-cell multi-omic topic embedding reveals cell-type-specific and COVID-19 severity-related immune signatures
Manqi Zhou, Hao Zhang, Zilong Bai, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 3
Artificial Intelligence Models for Cell Type and Subtype Identification Based on Single-Cell RNA Sequencing Data in Vision Science
Yeganeh Madadi, Aboozar Monavarfeshani, Hao Chen, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2023) Vol. 20, Iss. 5, pp. 2837-2852
Open Access | Times Cited: 3
Exploring multi-omics latent embedding spaces for characterizing tumor heterogeneity and tumoral fitness effects
Feng-ao Wang, Junwei Liu, Feng Gao, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 3
Structure-preserved dimension reduction using joint triplets sampling for multi-batch integration of single-cell transcriptomic data
Xinyi Xu, Xiangjie Li
Briefings in Bioinformatics (2023) Vol. 24, Iss. 1
Closed Access | Times Cited: 2
Unraveling dynamically-encoded latent transcriptomic patterns in pancreatic cancer cells by topic modelling
Yichen Zhang, Mohammadali Khalilitousi, Yongjin Y Park
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 2
NCAE: data-driven representations using a deep network-coherent DNA methylation autoencoder identify robust disease and risk factor signatures
David Martínez-Enguita, Sanjiv K. Dwivedi, Rebecka Jörnsten, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 5
Open Access | Times Cited: 2
Interpretable deep generative models for genomics
Yongin Choi, Ruoxin Li, Gerald Quon
bioRxiv (Cold Spring Harbor Laboratory) (2021)
Open Access | Times Cited: 5
Imputation method for single-cell RNA-seq data using neural topic model
Yueyang Qi, Shuangkai Han, Lin Tang, et al.
GigaScience (2022) Vol. 12
Open Access | Times Cited: 3
A highly scalable approach to topic modelling in single-cell data by approximate pseudobulk projection
Sishir Subedi, Tomokazu S. Sumida, Yongjin Park
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Transfer Learning Of Gene Expression Using Reactome
Siham Belgadi, David Y. Zhang, Ashwin Gopinath
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
A Message Passing Framework for Precise Cell State Identification with scClassify2
Wenze Ding, Yue Cao, Xiaohang Fu, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Closed Access
A variational autoencoder trained with priors from canonical pathways increases the interpretability of transcriptome data
Bin Liu, Bodo Rosenhahn, Thomas Illig, et al.
PLoS Computational Biology (2024) Vol. 20, Iss. 7, pp. e1011198-e1011198
Open Access
Comprehensive single-cell RNA-seq analysis using deep interpretable generative modeling guided by biological hierarchy knowledge
Hegang Chen, Yuyin Lu, Zhiming Dai, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 4
Open Access

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