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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1–24 of 99 filtered models
Spatial RNA-to-protein prediction turning transcriptomics-only sections into virtual protein maps via two adaptively reweighted graph channels.
Developmental time inference for single-cell and spatial transcriptomes, regressing absolute embryonic age from frozen Geneformer embeddings.
Tissue reconstruction model placing dissociated single cells into spatial coordinates by predicting pairwise distances in a learned embedding space.
Pan-cancer clinico-genomic model for treatment response and survival prediction, transferring zero-shot to unseen hospitals and cancer types.
Cell-level pathology foundation model that types every nucleus on a routine H&E slide, supervised by paired Xenium spatial transcriptomics.
Whole-transcriptome inference from label-free live-cell phase-contrast microscopy, predicting 18,085 genes without staining or lysing the cells.
Single-cell metabolome inference from scRNA-seq, learned from spatially paired Visium and MALDI-MSI sections by multiple-instance learning.
Virtual spatial transcriptomics model that infers spot-level gene expression from H&E slides by fusing tile, slide, and spatial-position features.
Histopathology foundation model predicting spatial gene expression from H&E slides at single-cell resolution via linear whole-slide attention.
Spatial transcriptomics foundation model giving gene-, cell- and neighborhood-scale embeddings zero-shot, plus in-silico gene knockout in tissue.
Spatial proteomics foundation model for multiplex immunofluorescence, with a 268-marker vocabulary and marker-conditioned 768-dimensional embeddings.
Spatial proteomics foundation model trained on over 51 million single cells to learn panel-robust cell representations across platforms.
Cell type annotation model mapping human single-cell and spatial transcriptomes onto one hierarchical typology of 381 types across 23 tissues.
Spatial proteomics prediction from routine H&E slides, generating 21-channel virtual multiplex immunofluorescence maps of the tumor microenvironment.
Spatial multi-omics integration model aligning RNA, protein, metabolomics, and histology to map cell-state gradients and cell-cell interactions.
Spatial transcriptomics prediction from histology using conditional neural fields to reconstruct continuous gene expression fields.
Histopathology model reconstructing tissue-wide single-cell gene expression from H&E slides, using sparse TMA measurements as molecular anchors.
Variational autoencoder trained on scRNA-seq and applied frozen to impute unmeasured genes and denoise spatial transcriptomics profiles.
Multimodal foundation model pretrained on 1.76B histology and spatial transcriptomics spots, inferring molecular state from whole-slide images.
Tri-modal foundation model unifying histology images, spatial transcriptomics, and language for zero-shot pathology and spatial biology reasoning.
Diffusion transformer for virtual tissue synthesis, generating H&E histopathology patches conditioned on spatial gene expression and morphology.
Spatial transcriptomics foundation model for the tumor microenvironment, giving TME-aware embeddings and in silico perturbation from one checkpoint.
Spatial transcriptomics deconvolution foundation model whose rank-based spot encoding transfers across tissues and platforms without retraining.