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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:

DynaMorph: self-supervised learning of morphodynamic states of live cells
Zhenqin Wu, Bryant B. Chhun, Galina Popova, et al.
Molecular Biology of the Cell (2022) Vol. 33, Iss. 6
Open Access | Times Cited: 30

Showing 1-25 of 30 citing articles:

Bridging live-cell imaging and next-generation cancer treatment
María Alieva, Amber K. L. Wezenaar, Ellen J. Wehrens, et al.
Nature reviews. Cancer (2023) Vol. 23, Iss. 11, pp. 731-745
Closed Access | Times Cited: 38
Machine learning inference of continuous single-cell state transitions during myoblast differentiation and fusion
Amit Shakarchy, Giulia Zarfati, Adi Hazak, et al.
Molecular Systems Biology (2024)
Open Access | Times Cited: 8
Learning biophysical determinants of cell fate with deep neural networks
Christopher J. Soelistyo, Giulia Vallardi, Guillaume Charras, et al.
Nature Machine Intelligence (2022) Vol. 4, Iss. 7, pp. 636-644
Open Access | Times Cited: 29
Orientation-invariant autoencoders learn robust representations for shape profiling of cells and organelles
James Burgess, Jeffrey Nirschl, Maria Clara Zanellati, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 7
Perspective on quantitative phase imaging to improve precision cancer medicine
Yang Liu, Shikhar Uttam
Journal of Biomedical Optics (2024) Vol. 29, Iss. S2
Open Access | Times Cited: 7
Caliban: Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning
Morgan Schwartz, Erick Moen, Geneva Miller, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2019)
Open Access | Times Cited: 50
Optofluidic imaging meets deep learning: from merging to emerging
Dickson M. D. Siu, Kelvin C. M. Lee, Bob M. F. Chung, et al.
Lab on a Chip (2023) Vol. 23, Iss. 5, pp. 1011-1033
Closed Access | Times Cited: 15
AI analysis of super-resolution microscopy: Biological discovery in the absence of ground truth
Ivan R. Nabi, Ben Cardoen, Ismail M. Khater, et al.
The Journal of Cell Biology (2024) Vol. 223, Iss. 8
Open Access | Times Cited: 5
Cerebral Organoids as an Experimental Platform for Human Neurogenomics
Tomasz J. Nowakowski, Sofie R. Salama
Cells (2022) Vol. 11, Iss. 18, pp. 2803-2803
Open Access | Times Cited: 21
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
Automated prediction of fibroblast phenotypes using mathematical descriptors of cellular features
Alex Khang, Abigail Barmore, Georgios Tseropoulos, et al.
Nature Communications (2025) Vol. 16, Iss. 1
Open Access
Artificial Intelligence-based Morphological Profiling for Drug Discovery: Advancing Towards 4D Phenotypic Profiling
Margreet R. de Vries, Lucas Dent, Hugh Sparks, et al.
Royal Society of Chemistry eBooks (2025), pp. 209-234
Closed Access
Deep-Manager: a versatile tool for optimal feature selection in live-cell imaging analysis
Arianna Mencattini, Michele D’Orazio, Paola Casti, et al.
Communications Biology (2023) Vol. 6, Iss. 1
Open Access | Times Cited: 10
Morphodynamical cell state description via live-cell imaging trajectory embedding
Jeremy Copperman, Sean M. Gross, Young Hwan Chang, et al.
Communications Biology (2023) Vol. 6, Iss. 1
Open Access | Times Cited: 10
Particulate matter composition drives differential molecular and morphological responses in lung epithelial cells
Sean M. Engels, Pratik Kamat, G. Stavros Pafilis, et al.
PNAS Nexus (2023) Vol. 3, Iss. 1
Open Access | Times Cited: 10
Machine learning enhanced cell tracking
Christopher J. Soelistyo, Kristina Ulicna, Alan R. Lowe
Frontiers in Bioinformatics (2023) Vol. 3
Open Access | Times Cited: 9
Robust virtual staining of landmark organelles
Ziwen Liu, Eduardo Hirata-Miyasaki, Soorya Pradeep, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 3
Learning dynamic image representations for self-supervised cell cycle annotation
Kristina Ulicna, Manasi Kelkar, Christopher J. Soelistyo, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 9
Cells in the 3D biomatrix on-chip: better mimicking the real micro-physiological system
Michele D’Orazio, Joanna Filippi, Gianni Antonelli, et al.
Next Materials (2024) Vol. 5, pp. 100229-100229
Open Access | Times Cited: 2
Interpretable Fine‐Grained Phenotypes of Subcellular Dynamics via Unsupervised Deep Learning
Chuangqi Wang, Hee June Choi, Lucy Woodbury, et al.
Advanced Science (2024)
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
AI-driven Classification of Cancer-Associated Fibroblasts Using Morphodynamic and Motile Features
Minwoo Kang, Chanhong Min, D. Somayadineshraj, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 1
Automated cell profiling in imaging flow cytometry with annotation-efficient learning
Tianqi Hong, Mao Peng, Younggy Kim, et al.
Optics & Laser Technology (2024) Vol. 181, pp. 111992-111992
Closed Access | Times Cited: 1
Morphological signatures of actin organization in single cells accurately classify genetic perturbations using CNNs with transfer learning
Sydney Alderfer, Jiangyu Sun, Lubna H. Tahtamouni, et al.
Soft Matter (2022) Vol. 18, Iss. 43, pp. 8342-8354
Closed Access | Times Cited: 6
Machine learning inference of continuous single-cell state transitions during myoblast differentiation and fusion
Amit Shakarchy, Giulia Zarfati, Adi Hazak, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 2
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