Author

Zoe Wefers

Stanford University

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 2 Cell Biology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1 Research Focus:clustering· 1 Approach:diffusion model· 1 Assay:fluorescence / live-cell microscopy· 1 Organism:human· 1

Frequent Co-Authors

Emma K. LundbergiD· 2 shared Alejandro LozanoiD· 1 shared Ankit GuptaiD· 1 shared Anthony J. CesnikiD· 1 shared Antoine TalyiD· 1 shared Chenyu WangiD· 1 shared Daisy Yi DingiD· 1 shared Dan LüiD· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 17 mutual co-author links

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2 shared papersBioinformatics

Emma K. LundbergORCID

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Author profile →

3 papers

Recent·Most cited

CellFluxV2: An Image Generative Foundation Model for Virtual Cell Modeling

Yuhui Zhang, Yuchang Su, Zoe Wefers, Shiye Su, He Li, Tianhong Li, Chenyu Wang, James Burgess, Alejandro Lozano, Linqi Zhou, Daisy Yi Ding, Jeffrey Nirschl, Emma K. Lundberg, Serena Y. Yeung

CellFluxV2 is an image-generative model that predicts cell morphology changes under perturbations, achieving higher image fidelity than baselines and enabling batch-effect correction and cell-state interpolation.

bioRxiv · Jan 20, 2026 · PreprintBioinformatics

Improving the reliability of molecular string representations for generative chemistry

Etienne Reboul, Zoe Wefers, Harish Prabakaran, Jérôme Waldispühl, Antoine Taly

This study evaluates SMILES and SELFIES as molecular representations for generative chemistry, finding that neither ensures both viability and fidelity, and introduces ClearSMILES to improve both.

Journal of Chemical Information and Modeling · Sep 17, 2025Peer-reviewedBioinformatics

SubCell: Proteome-aware vision foundation models for microscopy capture single-cell biology

Ankit Gupta, Zoe Wefers, Konstantin Kahnert, Jan N. Hansen, Mohini K Misra, William D. Leineweber, Anthony J. Cesnik, Dan Lü, Ulrika Axelsson, Frederic Ballllosera Navarro, Russ Biagio Altman, Theofanis Karaletsos, Emma K. Lundberg

SubCell is a deep learning model for fluorescence microscopy that captures cellular morphology and protein organization, outperforming existing methods and enabling a multimodal approach to gene function.

bioRxiv · Dec 6, 2024 · PreprintCell Biology