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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 25–48 of 237 filtered models
Single-cell cancer model scoring driver-associated expression programs by projecting scRNA-seq through axes frozen from genotype-matched bulk tumors.
Molecule generation conditioned on single-cell transcriptomes, designing cell-type-specific compounds that revert diseased cell states.
Cell type annotation model mapping human single-cell and spatial transcriptomes onto one hierarchical typology of 381 types across 23 tissues.
Genomics foundation model that represents individual DNA fragments in a learned semantic space for cell-free DNA cancer detection and cell typing.
Spatial multi-omics integration model aligning RNA, protein, metabolomics, and histology to map cell-state gradients and cell-cell interactions.
Single-cell perturbation-response model predicting transcriptomic and cell-number changes for unseen perturbations plus inverse design.
Contrastive multimodal model for perturbation screens, aligning transcriptomic signatures with text and cell-painting image embeddings.
Generative framework that learns a developmental vector field from scRNA-seq snapshots, coupling flow matching with molecular RNA kinetics.
Self-supervised 3D masked autoencoder for volumetric fluorescence microscopy, aligned to ESM2 embeddings to predict protein localization.
Single-cell language model that prepends biomedical knowledge-graph tokens to cell sentences, grounding cell type annotation in pathway structure.
Self-supervised foundation model for clinical flow cytometry, producing panel-agnostic specimen-level representations from multi-panel data.
Multimodal foundation model for precision neurology that reconstructs a patient's molecular brain state from blood to predict disease progression.
Gene representation framework fusing DNA, transcript, protein, text, and single-cell embeddings into one latent space that survives missing views.
860M-parameter generative single-cell foundation model that jointly represents and generates epigenomic, transcriptomic, and proteomic modalities.
Variational autoencoder trained on scRNA-seq and applied frozen to impute unmeasured genes and denoise spatial transcriptomics profiles.
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.