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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1–7 of 7 filtered models
Virtual cell model using masked discrete diffusion over the whole transcriptome to simulate scRNA-seq perturbation responses across tissues.
Virtual cell foundation model predicting single-cell responses to genetic, chemical, and cytokine perturbations with conditional flow matching.
Generative virtual-cell model predicting whole-transcriptome responses to unseen compounds and genetic perturbations, from cell lines to organoids.
Knowledge-graph-grounded model that predicts single-cell transcriptomic responses to small molecules, with zero-shot prediction for unprofiled drugs.
Transcriptome-guided diffusion model generating Cell Painting images for unseen perturbations, improving MOA retrieval accuracy by 16.9% over IMPA.
Single-cell epigenomic foundation model that reads scATAC-seq as cell sentences of accessible cCREs, pretrained on about 5 million human cells.
Latent diffusion model that paints high-resolution Cell Painting images of cells responding to a chemical compound or an over-expressed gene.