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CellFM is an 800-million-parameter single-cell model trained on 100 million human cells.
CellFM is a single-cell foundation model from a Chinese consortium, a transformer with efficient attention over gene tokens that scales to 800 million parameters, among the largest single-cell models by parameter count. The 2024 bioRxiv preprint trained it on 100 million human cells and reported gains on cell-type annotation and perturbation prediction tasks. It is a research model for computational biologists exploring whether scale alone improves single-cell prediction. Independent evaluation is limited, and the wider field has found that many single-cell foundation models barely beat simple baselines on perturbation tasks, so its reported gains need external replication. For a newcomer: CellFM is one of the biggest models of its kind, but outsiders have not yet confirmed how much the extra size helps.
CellFM is a transformer with efficient attention over gene tokens.
Query for this technology: (TITLE:"CellFM" OR ABSTRACT:"CellFM") AND (cancer OR tumor OR tumour OR oncology OR carcinoma OR lymphoma OR leukemia OR leukaemia OR myeloma OR sarcoma OR melanoma OR glioma). Results are unfiltered search hits about CellFM, not a curated reading list.
Shares the tags foundation-model, virtual-cell.
Shares the tags foundation-model, virtual-cell.
Shares the tags foundation-model, virtual-cell.
Shares the tags foundation-model, virtual-cell.
Shares the tags foundation-model, virtual-cell.
Shares the tags foundation-model, virtual-cell.
Shares the tags foundation-model, virtual-cell.
Shares the tags foundation-model, virtual-cell.
Open-source projects that implement or serve this technology, from OnCo's own catalogue: licence and last activity as the repository reported them on the day of the fetch. Listing is not endorsement; check the licence before reuse and the validation before clinical use.
A large single-cell foundation model trained on human transcriptomes from Sun Yat-sen University.