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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1–24 of 33 filtered models
Normative foundation model for structural brain MRI that scores how far each of 218 anatomical parcels departs from healthy aging.
Brain MRI morphometry framework turning one T1w scan into 13 descriptors and a normative deviation profile shared across 19 clinical diagnoses.
Single-cell cytometry model that tokenizes each cell as marker-expression pairs, letting studies with different antibody panels share one encoder.
Histopathology foundation model predicting spatial gene expression from H&E slides at single-cell resolution via linear whole-slide attention.
B-cell receptor DNA language model pretrained on antibody heavy-chain nucleotide sequences, with embeddings that outperform protein language models.
Brain MRI morphometry model that estimates cortical thickness, surface area, and volume in milliseconds instead of hours.
Genetically aligned foundation model for blood smear cytology that links single-cell morphology to the chromosomal aberrations behind AML and APL.
Self-supervised brain MRI foundation model built on DINOv3, pretrained on roughly 6.6 million unlabeled axial slices for neuroimaging tasks.
Structural brain MRI foundation model pretrained on synthetic healthy volumes only, giving frozen features for brain age, dementia risk and image QC.
Gut microbiome foundation model pretrained on human shotgun metagenomes, learning species-level taxonomic representations for disease prediction.
Hierarchical single-cell foundation model that turns scRNA-seq profiles into zero-shot donor-level embeddings for disease and biomarker prediction.
Foundation model for tandem mass spectrometry that embeds MS/MS spectra into a learned chemical space, resolving isomers and classifying disease.
Missense variant pathogenicity predictor that also ranks candidate diseases, aligning ESM-2 protein embeddings with PubMedBERT disease text.
Self-supervised foundation model for 3D brain MRI, learning transferable anatomical representations from unlabeled scans for disease classification.
Dermatology foundation model pretrained on 432,776 skin images, covering malignancy classification, severity grading, and lesion segmentation.
Retinal OCT vision-language model that writes layer-by-layer clinical summaries and assigns six-class disease labels from a single B-scan.
Chest CT masked autoencoder pretrained on over 5,000 volumes, fine-tuned to classify interstitial lung disease under Fleischner Society criteria.
Sparse autoencoder for blood-cell microscopy that decomposes hematology foundation model embeddings into expert-validated sub-cellular concepts.
Subject-level disease prediction from scRNA-seq, pairing cell-type-grouped scGPT pretraining with a Reactome pathway-constrained decoder.
Hypergraph foundation model for brain disease diagnosis from resting-state fMRI, self-supervised on high-order connectivity among brain regions.
Single-cell RNA-seq foundation models combining masked modeling with ontology supervision to classify cell states across unseen donors and diseases.
EEG foundation model pretrained by momentum contrastive learning and masked reconstruction, with a learnable channel mapping that unifies montages.
865M-parameter multimodal foundation model that fuses 3D low-dose chest CT with clinical data to answer 17 lung cancer screening questions.