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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 2137–2160 of 2565 models
Variational autoencoder pretrained on 74 million human single-cell transcriptomes from the CELLxGENE Census for batch correction and cell typing.
Semi-supervised cryo-ET segmentation framework that adapts DINOv2 vision transformers for 3D organelle annotation using sparse 2D slice labels.
Mitochondrial ultrastructure segmentation in electron microscopy volumes, resolving membranes and cristae with cross-sample domain adaptation.
Histopathology multimodal assistant answering questions about H&E patches, pairing a pathology-trained CLIP tower with a 13B Vicuna language model.
Generative biological foundation model placing DNA, RNA, and protein in one shared vocabulary, spanning genomic, proteomic, and cross-molecule tasks.
Histopathology vision-language model classifying H&E patches zero-shot from text prompts, trained on 1.6M captions written for whole-slide crops.
Open medical multimodal LLMs (7B and 34B) for visual question answering over radiology, pathology, and endoscopy images, trained on PubMedVision.
Self-supervised foundation model for 12-lead ECG, pairing masked autoencoder pretraining with contrastive regularization for robust diagnostics.
Multimodal generative protein language model reasoning jointly over protein sequence, structure, and function, trained at 98B parameters.
Self-supervised masked autoencoder for functional MRI that learns representations from BOLD time-series with per-ROI embeddings and graph attention.
DeepLabV3 segmentation model that separates tissue from glass background in H&E and IHC whole-slide images, as used by the HEST-Library.
Respiratory acoustic foundation models pretrained on roughly 136K cough and breathing recordings for disease detection and lung function estimation.
Blind peptide binder design from a protein sequence alone, evolving linear or cyclic binders against a frozen AlphaFold2 with no binding site given.
3D vision-transformer foundation model for multimodal neuroimage segmentation, pretrained self-supervised on brain MRI from 41,400 participants.
Fluorescence microscopy denoising for transient synaptic signals, done frame by frame so one checkpoint transfers across sensors and frame rates.
Scaling-law study of protein language models identifying compute-optimal training for causal and masked objectives on 939 million protein sequences.
Microsoft Research multimodal LLM for grounded chest X-ray report generation, localizing each described finding with bounding boxes on the image.
Single-cell transcriptomics foundation model with 100 million parameters, pretrained on over 50 million human scRNA-seq profiles for cell embeddings.
Essential-protein prediction for prokaryotic genomes, pairing a frozen ESM-2 encoder with a bi-LSTM ensemble that generalizes to unseen species.
Single-cell foundation model with 800M parameters trained on ~100 million human cells, for annotation, perturbation prediction, and gene analysis.