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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 49–72 of 237 filtered models
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.
Hierarchical language model for atlas-level cell-type annotation of scATAC-seq data that annotates new query datasets without retraining.
Generative single-cell foundation model trained on 100M+ transcriptomes that predicts how genetic perturbations reshape cell trajectories over time.
Temporal diffusion framework for single-cell developmental dynamics, interpolating and forecasting cell states from irregularly sampled time series.
Knowledge-graph-grounded model that predicts single-cell transcriptomic responses to small molecules, with zero-shot prediction for unprofiled drugs.
Knowledge-graph foundation model for drug repurposing, grounding a biomedical graph in cell-type-specific genetic associations to rank indications.
Diffusion model predicting single-cell responses to genetic or drug perturbations, generating over distributions to capture population variability.
Single-cell foundation model applying discrete diffusion directly to scRNA-seq counts, generating unconditional and perturbation-conditioned profiles.