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Every biological foundation model, evaluated and ranked by the bio.rodeo team
All-atom protein generation model that samples side chains, backbone, and sequence together from a single diffusion process over atom coordinates.
Template-guided protein design model that miniaturizes, diversifies, or expands a natural protein by decoding a fixed-size probabilistic encoding.
Neural spike decoding model whose Hebbian self-attention yields interpretable low-dimensional embeddings of electrophysiology and calcium imaging.
Multimodal ECG-language model aligning 12-lead waveforms with clinical report text for conversational cardiac diagnosis and report generation.
Foundation model for photoplethysmography (PPG) that learns quality-robust waveform representations for heart rate, blood pressure, and AF detection.
Open transformer foundation model for 12-lead electrocardiograms, pretrained on 1.5 million unlabeled ECGs with a wav2vec 2.0 self-supervised recipe.
Conditional diffusion model that generates P-type ATPase backbone conformations in a specified E1, E1P, E2P, or E2 functional state.
Chemical language model reading modified and cyclic peptides as SMILES, fine-tuned to predict passive membrane diffusion of macrocycles.
Conditional generator of bulk transcriptome and DNA methylation profiles, sampling tissue-, age- and species-matched synthetic omics samples.
A Wasserstein GAN that generates artificial human genomes in PCA space, synthesizing 65,535-SNP haplotypes for 26 worldwide populations.
Open-source, lightweight generalist vision-language foundation model for diverse biomedical imaging and text tasks.
Brain MRI foundation model family pretrained with anatomically informed contrastive learning for diagnosis and clinical score prediction.
Medical vision-language model trained on the MedTrinity-25M dataset, answering questions and generating text about radiology and histology images.
Enzyme redesign framework built on a structure-to-sequence protein network, scoring mutants and generating sequences without retraining.
Slide-level pathology foundation model that learns whole-slide embeddings by aligning multiple stains of the same tissue during pretraining.
Chest X-ray embedding model built on ELIXR, producing image and image-text embeddings for data-efficient and zero-shot radiograph classification.
Geometric deep learning model generating context-aware protein representations across 156 cell-type contexts from a multi-organ single-cell atlas.
SAM2-based foundation model that segments 2D and 3D medical images by treating volumes and image sets as video object tracking.
Text-to-text biological language model spanning molecules, proteins, and text, adding IUPAC names and multi-task instruction tuning to BioT5.
Ultrasound foundation model pretrained on over two million multi-organ images, transferring to segmentation, classification, and image enhancement.
De novo peptide binder design framework that targets specific motifs, including disordered regions and conserved epitopes, from target sequence alone.
Reshapes a frozen ESM-2 latent space by contrasting it against EC, GO, InterPro and Gene3D ontology tokens for function-aware protein embeddings.
Generates protein sequences from EC, GO, InterPro and Gene3D prompts by cross-attending an ESM-2 decoder onto an annotation transformer encoder.