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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 121–144 of 237 filtered models
Single-cell ATAC-seq foundation model pretrained on 2.8 million cells across 1.15 million chromatin regions via masked peak reconstruction.
Single-cell transcriptomics model fine-tuned on 1.12M CAR-T profiles to annotate T cell subtypes and predict therapy response and neurotoxicity.
Single-cell perturbation prediction by conditional flow matching, using one cell-type-conditioned model in place of a separate model per cell type.
Single-cell multi-omics foundation model whose three-stage pretraining and distillation yield RNA-and-ATAC-aware embeddings from RNA-only input.
Spatial transcriptomics foundation model pairing gene-scale cell embeddings with an SE(2) Transformer over cell coordinates, pretrained on 88M cells.
Cross-species brain spatial transcriptomics foundation model pretrained on 133M cells from human, macaque, marmoset, and mouse whole brains.
Zebrafish single-cell foundation model built on the Geneformer framework, producing frozen gene and cell embeddings for developmental analysis.
Single-cell transcriptomics foundation model that encodes gene regulatory network structure into self-attention through graph signal processing.
Multimodal single-cell foundation model pretrained on 4M+ co-assayed cells, predicting 382 surface proteins from transcriptomes alone, zero-shot.
Spatial transcriptomics foundation model pretrained on 22 million cells, encoding each cell with its neighbors for niche and density prediction.
Single-cell perturbation foundation model predicting transcriptomic responses to CRISPR and small-molecule interventions in cancer cells.
Single-cell perturbation-response model that predicts transcriptomic and imaging outcomes of unseen genetic perturbations via a VAE with attention.
Hierarchical immune cell type annotation for scRNA-seq, rendering expression as images for a CNN. 93.2% mean subtype accuracy over seven datasets.
Virtual cell transformer that predicts how cells respond to genetic, chemical, or signaling perturbations, generalizing to unseen cellular contexts.
Multimodal transformer predicting alternative splicing outcomes across C. elegans neuron subtypes, reaching Spearman ρ = 0.88 on held-out exons.
Single-cell perturbation response prediction using dual conditional diffusion bridges that link unpaired control and perturbed populations.
Multimodal drug-response model coupling cell and molecule foundation models, pretrained on 1.8M perturbation RNA-seq profiles over 22,000 compounds.
Spatial omics foundation model that represents tissue as a hierarchical graph of neighboring cells over per-cell gene co-expression networks.
Reasoning LLM for single-cell type annotation, mapping per-cell expression to labels with marker-by-marker chains of thought on one GPU.
Distribution-level representation learning that embeds whole cell populations, perturbation responses, and sequence sets, not individual data points.
Transcriptomic perturbation prediction across unseen single and double gene knockdowns and unseen cell lines, driven by gene-gene knowledge graphs.
Single-cell chromatin accessibility foundation model with genome-aware tokenization, pretrained on 1.97 million scATAC-seq cells across 30 tissues.
Subject-level disease prediction from scRNA-seq, pairing cell-type-grouped scGPT pretraining with a Reactome pathway-constrained decoder.