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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 Applications in Single-Cell Omics Data Analysis
Nafiseh Erfanian, A. Ali Heydari, Pablo Iáñez Picazo, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2021)
Open Access | Times Cited: 15

Showing 15 citing articles:

Transformer for one stop interpretable cell type annotation
Jiawei Chen, Hao Xu, Wanyu Tao, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 109
Machine Learning: A Suitable Method for Biocatalysis
Pedro Sampaio, Pedro Fernandes
Catalysts (2023) Vol. 13, Iss. 6, pp. 961-961
Open Access | Times Cited: 18
Past, current, and future of transcriptomics
Xinmin Li, Ilya Belalov, Anton Buzdin
Elsevier eBooks (2025), pp. 1-14
Closed Access
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
MAPLE: A Hybrid Framework for Multi-Sample Spatial Transcriptomics Data
Hyeongseon Jeon, Carter Allen, José Antonio Ovando-Ricárdez, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2022)
Open Access | Times Cited: 15
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
Deep Learning in Spatial Transcriptomics: Learning From the Next Next-Generation Sequencing
A. Ali Heydari, Suzanne Sindi
bioRxiv (Cold Spring Harbor Laboratory) (2022)
Open Access | Times Cited: 11
Opportunities and challenges for sample preparation and enrichment in mass spectrometry for single‐cell metabolomics
Dirk Wevers, Rawi Ramautar, Charles Clark, et al.
Electrophoresis (2023) Vol. 44, Iss. 24, pp. 2000-2024
Open Access | Times Cited: 6
Single-cell classification, analysis, and its application using deep learning techniques
R. Premkumar, Arthi Srinivasan, Kanika Devi, et al.
Biosystems (2024) Vol. 237, pp. 105142-105142
Closed Access | Times Cited: 2
Proteomic Alteration in the Progression of Multiple Myeloma: A Comprehensive Review
Nor Hayati Ismail, Ali Mussa, Mutaz Jamal Al-Khreisat, et al.
Diagnostics (2023) Vol. 13, Iss. 14, pp. 2328-2328
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
N-ACT: An Interpretable Deep Learning Model for Automatic Cell Type and Salient Gene Identification
A. Ali Heydari, Oscar A. Davalos, Katrina K. Hoyer, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2022)
Open Access | Times Cited: 3
Boosting Single-Cell RNA Sequencing Analysis with Simple Neural Attention
Oscar A. Davalos, A. Ali Heydari, Elana J. Fertig, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 1
A Deep Learning-Based Method Facilitates scRNA-seq Cell Type Identification
Xin Wang, Zhuo Li, Jie Han, et al.
Communications in computer and information science (2024), pp. 171-185
Closed Access
Novel Representation Learning Improves Personalizing Blood Test Ranges and Disease Risk Prediction
A. Ali Heydari, Javier L. Prieto, Shwetak Patel, et al.
Research Square (Research Square) (2023)
Open Access
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