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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:

Predicting drug polypharmacology from cell morphology readouts using variational autoencoder latent space arithmetic
Yuen Ler Chow, Shantanu Singh, Anne E. Carpenter, et al.
PLoS Computational Biology (2022) Vol. 18, Iss. 2, pp. e1009888-e1009888
Open Access | Times Cited: 33

Showing 1-25 of 33 citing articles:

Integrating cell morphology with gene expression and chemical structure to aid mitochondrial toxicity detection
Srijit Seal, Jordi Carreras‐Puigvert, Maria‐Anna Trapotsi, et al.
Communications Biology (2022) Vol. 5, Iss. 1
Open Access | Times Cited: 65
Merging bioactivity predictions from cell morphology and chemical fingerprint models using similarity to training data
Srijit Seal, Hongbin Yang, Maria‐Anna Trapotsi, et al.
Journal of Cheminformatics (2023) Vol. 15, Iss. 1
Open Access | Times Cited: 26
From pixels to phenotypes: Integrating image-based profiling with cell health data as BioMorph features improves interpretability
Srijit Seal, Jordi Carreras‐Puigvert, Shantanu Singh, et al.
Molecular Biology of the Cell (2024) Vol. 35, Iss. 3
Open Access | Times Cited: 12
Predicting cell morphological responses to perturbations using generative modeling
Alessandro Palma, Fabian J. Theis, Mohammad Lotfollahi
Nature Communications (2025) Vol. 16, Iss. 1
Open Access | Times Cited: 1
Self-supervision advances morphological profiling by unlocking powerful image representations
Vladislav Kim, Nikolas Adaloglou, Marc Osterland, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 21
Unbiased single-cell morphology with self-supervised vision transformers
Michael Doron, Théo Moutakanni, Zitong Chen, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 21
Evolution and impact of high content imaging
Gregory P. Way, Heba Sailem, Steven Shave, et al.
SLAS DISCOVERY (2023) Vol. 28, Iss. 7, pp. 292-305
Open Access | Times Cited: 21
Using Transcriptomics and Cell Morphology Data in Drug Discovery: The Long Road to Practice
Lavinia-Lorena Pruteanu, Andreas Bender
ACS Medicinal Chemistry Letters (2023) Vol. 14, Iss. 4, pp. 386-395
Open Access | Times Cited: 14
Unlocking the Future of Drug Development: Generative AI, Digital Twins, and Beyond
Zamara Mariam, Sarfaraz K. Niazi, Matthias Magoola
BioMedInformatics (2024) Vol. 4, Iss. 2, pp. 1441-1456
Open Access | Times Cited: 4
Morphological profiling for drug discovery in the era of deep learning
Qiaosi Tang, Ranjala Ratnayake, Gustavo Seabra, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 4
Open Access | Times Cited: 4
Self-supervision advances morphological profiling by unlocking powerful image representations
Vladislav Kim, Nikolas Adaloglou, Marc Osterland, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Addressing persistent challenges in digital image analysis of cancer tissue: resources developed from a hackathon
Sandhya Prabhakaran, Clarence Yapp, Gregory J. Baker, et al.
Molecular Oncology (2025)
Open Access
uniDINO: assay-independent feature extraction for fluorescence microscopy images
Flavio M. Morelli, Vladislav Kim, Franziska Annabelle Hecker, et al.
Computational and Structural Biotechnology Journal (2025) Vol. 27, pp. 928-936
Open Access
A review of the Applications, Benefits, and challenges of Generative AI for sustainable toxicology
Furqan Alam, Tahani Saleh Mohammed Alnazzawi, Rashid Mehmood, et al.
Current Research in Toxicology (2025), pp. 100232-100232
Open Access
Morphological Profiling in 2D High Content Screening Assays – Case Studies and Applications
Rebecca E. Graham
Royal Society of Chemistry eBooks (2025), pp. 125-146
Closed Access
Artificial-intelligence-driven innovations in mechanistic computational modeling and digital twins for biomedical applications
Bhanwar Lal Puniya
Journal of Molecular Biology (2025), pp. 169181-169181
Closed Access
Predicting cell morphological responses to perturbations using generative modeling
Alessandro Palma, Fabian J. Theis, Mohammad Lotfollahi
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 9
Scalable and universal prediction of cellular phenotypes
Yuge Ji, Alejandro Tejada-Lapuerta, Niklas A. Schmacke, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 3
Machine learning in microscopy – insights, opportunities and challenges
Inês Cunha, Emma Latron, Sebastian Bauer, et al.
Journal of Cell Science (2024) Vol. 137, Iss. 20
Closed Access | Times Cited: 3
Cell Painting: a decade of discovery and innovation in cellular imaging
Srijit Seal, Maria‐Anna Trapotsi, Ola Spjuth, et al.
Nature Methods (2024)
Closed Access | Times Cited: 3
Cross‐Modal Graph Contrastive Learning with Cellular Images
Shuangjia Zheng, Jiahua Rao, Jixian Zhang, et al.
Advanced Science (2024) Vol. 11, Iss. 32
Open Access | Times Cited: 2
Single-cell morphodynamical trajectories enable prediction of gene expression accompanying cell state change
Jeremy Copperman, Ian C. McLean, Sean M. Gross, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 1
Decoding phenotypic screening: A comparative analysis of image representations
Adriana Borowa, Dawid Rymarczyk, Marek Żyła, et al.
Computational and Structural Biotechnology Journal (2024) Vol. 23, pp. 1181-1188
Open Access | Times Cited: 1
Unlocking the Future of Drug Development: Generative AI, Digital Twins, and Beyond
Zamara Mariam, Sarfaraz K. Niazi, Matthias Magoola
(2024)
Open Access | Times Cited: 1
Network depth affects inference of gene sets from bacterial transcriptomes using denoising autoencoders
Willow Kion-Crosby, Lars Barquist
Bioinformatics Advances (2024) Vol. 4, Iss. 1
Open Access | Times Cited: 1
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