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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 49–72 of 250 filtered models
TCR-epitope binding prediction and tumor-reactive T-cell identification in one heterogeneous graph transformer, reaching AUROC 0.937 on IEDB.
Transcriptomics foundation model from Recursion that masks and reconstructs RNA-seq gene expression counts to learn reusable sample embeddings.
Zebrafish sequence-to-function model predicting cell-type-specific gene expression from DNA sequence across embryonic development.
Cell world model pretrained on a 2.4M-cell mouse embryonic atlas, predicting one-step transcriptional state transitions and perturbation response.
Flow-matching framework that translates omics signatures across biological domains, such as mouse to human transcriptomics, without paired samples.
Generative model that reconstructs single-cell spatial coordinates from scRNA-seq guided by spatial transcriptomics, without cell-type labels.
Virtual cell foundation model pretrained on over 23 million cells from 5,000 patient samples for drug target and biomarker discovery.
Causal multimodal transformer that embeds the do-operator in attention to predict single-cell gene expression under unseen genetic perturbations.
Multi-modal transformer fusing LLM gene embeddings with biological knowledge graphs to predict single-cell responses to genetic perturbations.
Meta-learning framework that transfers perturbation responses across cell lines, donors, and drugs from a few measured seed perturbations.
Direction-aware foundation model trained on bulk RNA-seq differential-expression profiles to simulate coordinated gene dynamics in viral infection.
Single-cell foundation model that tokenizes scRNA-seq into 10 tokens in a Qwen3-4B vocabulary for cell type annotation and perturbation prediction.
Transcriptome foundation model for precision oncology, generalizing zero-shot across tissue, plasma cfRNA, and tumor-educated platelet modalities.
Transcriptomics-native single-cell foundation model that learns batch-invariant cell representations and probabilistically generates virtual cells.
Single-cell multiomic foundation model that transfers pan-cancer RNA-ATAC regulatory structure into RNA-only tumour datasets via low-rank adapters.
Flow cytometry foundation model that reads heterogeneous antibody panels through a universal marker embedding to predict sample-level phenotypes.
Single-cell foundation model combining regulatory network priors with a Mamba backbone for clustering, batch integration, and perturbation modeling.
Generates single-cell transcriptomes from structured biological metadata via contrastive language-omics pretraining and a diffusion transformer.
Virtual cell model using masked discrete diffusion over the whole transcriptome to simulate scRNA-seq perturbation responses across tissues.
Generative adversarial network trained on single-cell and bulk RNA-seq for sample stratification, marker analysis, and synthetic data generation.
Virtual cell foundation model predicting single-cell responses to genetic, chemical, and cytokine perturbations with conditional flow matching.
Diffusion language model with 4.9 billion parameters that predicts genome-wide CRISPRi perturbation responses in single-cell transcriptomes.
Multimodal foundation model integrating spatial transcriptomics, H&E histopathology, and pathway scores for single-cell niche discovery.
Generative virtual-cell model predicting whole-transcriptome responses to unseen compounds and genetic perturbations, from cell lines to organoids.