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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 217–240 of 341 filtered models
De novo atomic model building from cryo-EM density maps, adapting AlphaFold2 with local attention and a 3D rotary position embedding.
Cell Painting microscopy foundation model, a channel-agnostic masked autoencoder producing morphological embeddings for zero-shot phenotypic analysis.
Cell Painting image generation conditioned on a control well image and a compound's structure, covering cell lines and chemicals never trained on.
Cryo-EM foundation model pre-trained on 65 million particle images, enabling zero-shot classification, pose clustering, and quality assessment.
Imaging-genetics foundation model pairing SNP genotypes with brain-MRI phenotypes by contrastive learning to surface many-to-many associations.
Cell microscopy foundation model with a 1.9-billion-parameter masked autoencoder producing embeddings that stay consistent across screening batches.
Cell phenotyping model for spatial proteomics using a language-informed vision transformer to classify cell types zero-shot across marker panels.
Protein subcellular localization from sequence, returning both a text label and a synthetic fluorescence image of the protein inside a given nucleus.
Masked-autoencoder foundation model that pre-trains a 3D Residual Encoder U-Net on roughly 39,000 brain MRIs for volumetric image segmentation.
Self-supervised vision transformer pretrained on chest X-rays to produce a domain-specific foundation model for classification and lung segmentation.
Spot detection and quantification in 5D fluorescence microscopy. Pretrained 2D and 3D U-Nets segment foci, then Gaussian fitting measures each one.
Multimodal large language model that interprets 12-lead electrocardiogram images, answering open-ended clinical questions and generating ECG reports.
Diffusion model translating in both directions between protein sequences and fluorescence microscopy images to predict subcellular localization.
3D CT vision-language model that drafts radiology reports, answers questions about volumes, and screens for disease from a masked-autoencoder encoder.
Computed tomography embedding model that compresses a whole DICOM CT volume into a 1,408-number vector for data-efficient downstream classifiers.
Vision-language chat model for 3D chest CT volumes, answering free-form questions and drafting radiology report findings from a frozen 3D encoder.
Self-supervised contrastive model embedding cell and organelle dynamics from time-lapse microscopy for cell-state analysis without manual labels.
Echocardiography vision foundation model self-distilled on 20 million ultrasound images from 11 clinical centres, with swappable task decoders.
CT segmentation foundation model that uses task prompts to segment 83 anatomical structures and lesions across whole-body scans in a single network.
Masked-autoencoder foundation model for chest radiographs, self-supervised on 1.04 million unlabelled images for disease screening and localization.
Generative foundation model for cryo-EM density maps using flow matching, enabling zero-shot denoising, map sharpening, and missing wedge restoration.
Medical imaging embedding model spanning X-ray, CT, MRI, dermoscopy, OCT, fundus, ultrasound, histopathology and mammography in one encoder.
Cardiac MR vision foundation model self-supervised on 36 million images, fine-tuned for segmentation, view classification and pathology detection.
Domain-aware multi-task pretrained 3D Swin Transformer for T1-weighted brain MRI, transferring to Alzheimer's, Parkinson's and brain age tasks.