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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 169–192 of 262 models
Chest X-ray embedding model built on ELIXR, producing image and image-text embeddings for data-efficient and zero-shot radiograph classification.
Geometric deep learning model generating context-aware protein representations across 156 cell-type contexts from a multi-organ single-cell atlas.
SAM2-based foundation model that segments 2D and 3D medical images by treating volumes and image sets as video object tracking.
Text-to-text biological language model spanning molecules, proteins, and text, adding IUPAC names and multi-task instruction tuning to BioT5.
De novo peptide binder design framework that targets specific motifs, including disordered regions and conserved epitopes, from target sequence alone.
Single-cell foundation model pre-trained on 50 million cells for gene network inference, denoising, and cell type prediction.
Framework turning single-cell expression profiles into ranked gene-name sequences, letting off-the-shelf language models generate and annotate cells.
Histopathology vision transformer with 1.1B parameters, pretrained on patches from 500,000 H&E whole-slide images across 4,000 clinical practices.
Variational autoencoder pretrained on 74 million human single-cell transcriptomes from the CELLxGENE Census for batch correction and cell typing.
Open medical multimodal LLMs (7B and 34B) for visual question answering over radiology, pathology, and endoscopy images, trained on PubMedVision.
Multimodal generative protein language model reasoning jointly over protein sequence, structure, and function, trained at 98B parameters.
Respiratory acoustic foundation models pretrained on roughly 136K cough and breathing recordings for disease detection and lung function estimation.
Scaling-law study of protein language models identifying compute-optimal training for causal and masked objectives on 939 million protein sequences.
Single-cell transcriptomics foundation model with 100 million parameters, pretrained on over 50 million human scRNA-seq profiles for cell embeddings.
Single-cell foundation model with 800M parameters trained on ~100 million human cells, for annotation, perturbation prediction, and gene analysis.
Microsoft Research multimodal LLM for grounded chest X-ray report generation, localizing each described finding with bounding boxes on the image.
Tri-modal protein language model aligning sequence, structure, and text in one embedding space for natural-language search over billions of proteins.
Multimodal protein language model extending ESM-2 and SaProt with a Structure Adapter over residue torsion angles for protein function prediction.
Whole-slide histopathology foundation model pretrained on 1.3 billion image tiles from 171,189 clinical slides spanning 31 tissue types.
Vision-language foundation model pre-trained on screening mammogram-report pairs to improve data efficiency and robustness in breast cancer detection.
Universal brain lesion segmentation for multi-modal brain MRI, using a Mixture of Modality Experts to span diverse modalities and lesion types.
Generalist medical vision-language foundation model with 40B parameters, spanning radiology, pathology, dermatology, retinography, and endoscopy.