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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Histopathology vision-language foundation model pretrained on 1.17 million image-caption pairs with contrastive and captioning objectives.
Computational pathology foundation model (ViT-L/16, DINOv2) pretrained on over 100 million H&E tiles from more than 100,000 whole-slide images.
Self-supervised CNN pretrained on 700,000 person-days of UK Biobank accelerometer data for human activity recognition across devices and cohorts.
RNA language model that builds base-pairing constraints into self-attention, pretrained on 20.4 million sequences for structure and function tasks.
Bulk tumor transcriptome model ensembling hundreds of variational autoencoders into interpretable cancer-specific latent spaces for 18 cancers.
3D molecule generation that models atom coordinates and element types as distribution parameters updated by Bayesian inference, not by denoising.
Protein language models pretrained on Rosetta biophysics simulations rather than evolutionary data, then finetuned on small experimental assays.
Self-supervised 3D CT foundation model that extracts general-purpose tumor representations for cancer imaging biomarker discovery and prognosis.
Inverse RNA folding from contact maps: an axial-attention transformer designing sequences for pseudoknots, non-canonical pairs and multiplets.
Foundation model for medical image registration that aligns CT and MRI across anatomies and modalities without per-pair optimization or retraining.
Deep network that predicts structures of full biological assemblies: proteins, nucleic acids, small molecules, metals, and covalent modifications.
Antimicrobial peptide generator running denoising diffusion in the continuous ESM-2 embedding space, validated in mouse infection models.
Bidirectional, reverse-complement equivariant DNA language models built on Mamba state space models for long-range variant effect prediction.
Self-supervised foundation model for functional brain network analysis from resting-state fMRI, pretrained across 30 datasets for disorder diagnosis.
Blind protein-ligand docking that transfers to binding domains absent from training, scoring 22.6% top-1 on DockGen and 50% on PoseBusters.
Self-supervised pretraining framework for 3D medical image encoders that learns anatomy by predicting where a sub-volume sits within a CT scan.
Protein large language model adapted from LLaMA-2 that unifies sequence generation and superfamily classification in one 7B-parameter framework.
RNA foundation model trained on chemical-mapping data from millions of sequences, predicting reactivity, secondary structure, and degradation.
Generative pretrained transformer trained on 33 million human cells for single-cell annotation, batch correction, and perturbation prediction.
Fine-grained cell-type abundance prediction from H&E histology, transferring to unseen cohorts and large slide archives without any retraining.