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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:

Generative modeling of single-cell gene expression for dose-dependent chemical perturbations
Omar Kana, Rance Nault, David Filipovic, et al.
Patterns (2023) Vol. 4, Iss. 8, pp. 100817-100817
Open Access | Times Cited: 12

Showing 12 citing articles:

A mini-review on perturbation modelling across single-cell omic modalities
George Gavriilidis, Vasileios Vasileiou, Aspasia Orfanou, et al.
Computational and Structural Biotechnology Journal (2024) Vol. 23, pp. 1886-1896
Open Access | Times Cited: 9
scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network
Chengyu Li, Xin Shao, Shujing Zhang, et al.
Cell Reports Medicine (2024) Vol. 5, Iss. 6, pp. 101568-101568
Open Access | Times Cited: 9
Machine learning to dissect perturbations in complex cellular systems
Pablo Monfort-Lanzas, Katja Rungger, Leonie Madersbacher, et al.
Computational and Structural Biotechnology Journal (2025) Vol. 27, pp. 832-842
Open Access
Neural Network-Assisted Humanization of COVID-19 Hamster scRNAseq Data Reveals Matching Severity States in Human Disease
Vincent D. Friedrich, Peter Pennitz, Emanuel Wyler, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 2
Biologically Interpretable VAE with Supervision for Transcriptomics Data Under Ordinal Perturbations
Seyednami Niyakan, Byung-Jun Yoon, Xiaoning Qian, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 2
Unique challenges and best practices for single cell transcriptomic analysis in toxicology
David Filipovic, Omar Kana, Daniel Marri, et al.
Current Opinion in Toxicology (2024) Vol. 38, pp. 100475-100475
Closed Access | Times Cited: 1
scPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanism
Qun Jiang, Shengquan Chen, Xiaoyang Chen, et al.
Bioinformatics (2024) Vol. 40, Iss. 5
Open Access | Times Cited: 1
Toward subtask-decomposition-based learning and benchmarking for predicting genetic perturbation outcomes and beyond
Yicheng Gao, Zhiting Wei, Kejing Dong, et al.
Nature Computational Science (2024)
Closed Access | Times Cited: 1
Predicting transcriptional responses to novel chemical perturbations using deep generative model for drug discovery
Xiaoning Qi, Lianhe Zhao, Chenyu Tian, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 1
Ouroboros: cross-linking protein expression perturbations and cancer histology imaging with generative-predictive modeling
Srijay Deshpande, Sokratia Georgaka, Michael Haley, et al.
Bioinformatics (2024) Vol. 40, Iss. Supplement_2, pp. ii174-ii181
Open Access
Artificial intelligence and deep learning in single-cell omics data analysis: A case study
Zubina Anjum, Waniya Khalid, Gurupriya Takkar, et al.
Elsevier eBooks (2024), pp. 349-383
Closed Access
PrePR-CT: Predicting Perturbation Responses in Unseen Cell Types Using Cell-Type-Specific Graphs
Reem Alsulami, Robert Lehmann, Sumeer Ahmad Khan, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
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