Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

SMILE: mutual information learning for integration of single-cell omics data
Yang Xu, Priyojit Das, Rachel Patton McCord
Bioinformatics (2021) Vol. 38, Iss. 2, pp. 476-486
Open Access | Times Cited: 37

Showing 1-25 of 37 citing articles:

Deep learning applications in single-cell genomics and transcriptomics data analysis
Nafiseh Erfanian, A. Ali Heydari, Adib Miraki Feriz, et al.
Biomedicine & Pharmacotherapy (2023) Vol. 165, pp. 115077-115077
Open Access | Times Cited: 60
Multimodal data integration for oncology in the era of deep neural networks: a review
Asim Waqas, Aakash Tripathi, Ravi P. Ramachandran, et al.
Frontiers in Artificial Intelligence (2024) Vol. 7
Open Access | Times Cited: 23
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
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: 45
Multimodal deep learning approaches for single-cell multi-omics data integration
Tasbiraha Athaya, Rony Chowdhury Ripan, Xiaoman Li, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 5
Open Access | Times Cited: 33
A universal framework for single-cell multi-omics data integration with graph convolutional networks
Hongli Gao, Bin Zhang, Long Liu, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 3
Open Access | Times Cited: 21
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
Diagonal integration of multimodal single-cell data: potential pitfalls and paths forward
Yang Xu, Rachel Patton McCord
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 19
Application of Deep Learning for Single Cell Multi-Omics: A State-of-the-Art Review
Shahid Ahmad Wani, Sumeer Ahmad Khan, S. M. K. Quadri
Archives of Computational Methods in Engineering (2025)
Closed 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
sciCAN: single-cell chromatin accessibility and gene expression data integration via cycle-consistent adversarial network
Yang Xu, Edmon Begoli, Rachel Patton McCord
npj Systems Biology and Applications (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 17
The performance of deep generative models for learning joint embeddings of single-cell multi-omics data
Eva Brombacher, Maren Hackenberg, Clemens Kreutz, et al.
Frontiers in Molecular Biosciences (2022) Vol. 9
Open Access | Times Cited: 16
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
Sparsely Connected Autoencoders: A Multi-Purpose Tool for Single Cell omics Analysis
Luca Alessandrì, Maria Luisa Ratto, Sandro Gepiro Contaldo, et al.
International Journal of Molecular Sciences (2021) Vol. 22, Iss. 23, pp. 12755-12755
Open Access | Times Cited: 16
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
CoGO: a contrastive learning framework to predict disease similarity based on gene network and ontology structure
Yuhao Chen, Yanshi Hu, Xiaotian Hu, et al.
Bioinformatics (2022) Vol. 38, Iss. 18, pp. 4380-4386
Closed Access | Times Cited: 11
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
Improved quality metrics for association and reproducibility in chromatin accessibility data using mutual information
Cullen Roth, Vrinda Venu, Vanessa Job, et al.
BMC Bioinformatics (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 5
MASI enables fast model-free standardization and integration of single-cell transcriptomics data
Yang Xu, Rafael Kramann, Rachel Patton McCord, et al.
Communications Biology (2023) Vol. 6, Iss. 1
Open Access | Times Cited: 4
Improving cell type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning
Ibrahim Alsaggaf, Daniel Buchan, Cen Wan
Briefings in Functional Genomics (2024) Vol. 23, Iss. 4, pp. 441-451
Open Access | Times Cited: 1
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
Transmorph: a unifying computational framework for modular single-cell RNA-seq data integration
Aziz Fouché, Loïc Chadoutaud, Olivier Delattre, et al.
NAR Genomics and Bioinformatics (2023) Vol. 5, Iss. 3
Open Access | Times Cited: 3
Omics data integration in computational biology viewed through the prism of machine learning paradigms
Aziz Fouché, Andreï Zinovyev
Frontiers in Bioinformatics (2023) Vol. 3
Open Access | Times Cited: 3
The performance of deep generative models for learning joint embeddings of single-cell multi-omics data
Eva Brombacher, Maren Hackenberg, Clemens Kreutz, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2022)
Open Access | Times Cited: 4
Deep Learning and Machine Learning Applications in Biomedicine
Peiyi Yan, Yaojia Liu, Yuran Jia, et al.
Applied Sciences (2023) Vol. 14, Iss. 1, pp. 307-307
Open Access | Times Cited: 2
Page 1 - Next Page

Scroll to top