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
ProteinDT
UC Berkeley
A multimodal framework for text-guided protein design, enabling sequence generation, zero-shot editing, and property prediction via contrastive learning.
Cryo-IEF
Westlake University
Foundation model pre-trained on 65M cryo-EM particle images via contrastive learning, enabling zero-shot classification, pose clustering, and quality assessment.
Orthrus
Bowang Lab
Mamba-based mature RNA foundation model using contrastive learning on splice isoforms and 400+ mammalian species orthologs for mRNA property prediction.
ProTrek
Westlake University
Tri-modal protein language model unifying sequence, structure, and function via contrastive learning, enabling natural-language protein search across billions of entries.
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.
DNABERT-S
MAGICS Lab
Species-aware DNA embedding model built on DNABERT-2, using contrastive learning to cluster and differentiate genomic sequences by species without labeled data.
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.
ProtST
DeepGraphLearning
Multi-modal protein language model that jointly learns from protein sequences and biomedical text, enabling zero-shot functional prediction and retrieval.