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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 49–72 of 99 filtered models
Multi-modal contrastive model that aligns H&E histopathology with spatial transcriptomics across tissue scales to predict gene expression from images.
Multimodal foundation model that distills Evo 2 into a compact encoder guided by Hi-C data, predicting cell-type-specific 3D genome architecture.
Histopathology model that predicts single-cell type composition and reconstructs spatial gene expression from H&E slides, with no molecular assay.
Vision Transformer foundation model for spatial metabolomics, pretrained on ~4,000 curated METASPACE mass spectrometry imaging datasets.
Single-molecule localisation microscopy encoder that embeds nanoscale point clouds into a 128-dimension latent space to compare protein architecture.
Spatial gene expression prediction from H&E histology that decodes raw integer counts coarse to fine instead of regressing log-transformed values.
Kidney-specialized single-cell foundation model trained across four mammalian species for zero-shot cell-type annotation and batch integration.
Predicts virtual single-cell spatial transcriptomics from H&E histology using frozen pathology foundation models and spot-level supervision.
Histology vision transformer with 80M parameters that predicts spatial gene expression from H&E tissue images and transfers to tumor detection.
Contrastive alignment framework that projects H&E histology and single-cell transcriptomic foundation model embeddings into one shared latent space.
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.
Spatial proteomics imputation model inferring surface protein abundance from transcriptomics-only tissue sections via dual graph attention networks.
Spatial transcriptomics foundation model pretrained on 22 million cells, encoding each cell with its neighbors for niche and density prediction.
Spatial transcriptomics resolution enhancement from expression alone, using a tri-oriented Mamba encoder to predict expression between capture spots.
Spatial omics foundation model that represents tissue as a hierarchical graph of neighboring cells over per-cell gene co-expression networks.
Spatial proteomics foundation model, marker-aware and panel-agnostic, pretrained on 47 million multiplexed tissue-imaging patches from 175 markers.
Spatial transcriptomics prediction from H&E whole-slide images. One generative checkpoint covers 38,984 genes and 17 organs without fine-tuning.
Spatial transcriptomics foundation model pretrained to generate a cell's expression profile from its neighbors, yielding zero-shot niche embeddings.
Cell segmentation for image-based spatial transcriptomics that fuses RNA point clouds with any number of membrane and nuclear staining channels.
Patch-based 3D diffusion model that generates teravoxel-scale virtual mouse brain volumes conditioned on spatially resolved gene expression.
Histology-anchored framework pairing an H&E foundation model with a cellular hypergraph to predict single-cell multi-omics from tissue images.