All Competitors
Every biological foundation model, evaluated and ranked by the bio.rodeo team
Applications
Architectures
Learning Paradigms
Biological Subjects
Showing 1–9 of 9 filtered models
TranscriptFormer
Chan Zuckerberg Initiative
A generative cross-species foundation model for single-cell transcriptomics, trained on 112 million cells from 12 species spanning 1.5 billion years of evolution.
Evo
Arc Institute
A 7B parameter genomic foundation model using StripedHyena architecture to model prokaryotic DNA, RNA, and proteins at single-nucleotide resolution with 131k token context.
CryoSAM
Xu Lab
Training-free cryo-ET tomogram segmentation that adapts SAM and DINOv2 for 3D volumetric data, enabling full tomogram segmentation from a single user prompt.
CONCH
Mahmood Lab / Brigham and Women's Hospital
Vision-language foundation model for computational pathology, pretrained on 1.17M histopathology image-caption pairs with contrastive and captioning objectives.
UCE
Stanford University
Zero-shot foundation model for single-cell gene expression that generates species-agnostic cell embeddings using protein language model representations of gene products.
CellSAM
Van Valen Lab
Universal cell segmentation model adapting Meta's SAM for biology. Segments mammalian cells, yeast, and bacteria across diverse imaging modalities with human-level accuracy.
GPN
Song Lab
A DNA language model for unsupervised genome-wide variant effect prediction, trained on multispecies genomes via masked language modeling without functional annotation labels.
PLIP
Stanford University
CLIP-based vision-language foundation model for pathology, fine-tuned on 208,414 image-text pairs. Enables zero-shot tissue classification and image retrieval.
BiomedCLIP
Microsoft Research
Multimodal biomedical foundation model trained on 15M PubMed Central figure-caption pairs via contrastive learning, achieving state-of-the-art zero-shot performance across imaging modalities.