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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 2377–2400 of 2565 models
Structure-conditioned graph transformer trained with masked language modeling to learn residue encodings for inverse folding and antibody design.
RNA foundation model trained on 1 billion sequences, with a 400M-parameter variant for secondary and tertiary structure and functional annotation.
GPT-style DNA foundation model trained on over 200 billion base pairs of mammalian genomes for sequence generation, classification, and regression.
De novo protein design diffusion model that generates backbone structures conditioned on binding targets, symmetry constraints, and functional motifs.
Multimodal protein foundation model spanning sequence, structure, function, and evolution, used for de novo design and structure prediction.
Generative transformer that translates single-cell transcriptomes into proteomes, inferring missing protein abundance from RNA expression alone.
Tokenized graph transformer that embeds longitudinal brain functional connectomes from fMRI for interpretable neurodegenerative disease diagnosis.
De novo ligand design from a target protein sequence, writing SMILES autoregressively over a merged protein-ligand BPE vocabulary.
Endoscopy video foundation model that learns spatial-temporal representations from unlabeled clips for classification, segmentation, and detection.
Self-supervised foundation model for 3D medical image segmentation, pretrained on roughly 110,000 unannotated CT volumes via Volume Fusion.
Genomic foundation model built on the Hyena operator, processing DNA at single-nucleotide resolution with context windows up to 1 million tokens.
Multi-species genomic foundation model swapping k-mer tokenization for byte pair encoding, matching Nucleotide Transformer with 21x fewer parameters.
3D CT localization foundation model that pairs MedLAM with SAM to segment any anatomical structure at a fixed, dataset-independent annotation cost.
All-atom peptide conformational sampling from sequence, using hypernetwork-conditioned diffusion trained by energy against a molecular force field.
Enzyme optimum pH prediction from sequence, ensembling a light-attention network and a support vector regression over frozen ESM-1v embeddings.
Equivariant heterogeneous graph network that rescores docked protein-ligand poses for virtual screening and ranks structural analogs by activity.
Self-supervised vision foundation model pretrained on 1.3M medical images via second-order graph matching, for segmentation and classification.
Vision Transformer for cell instance segmentation and classification in H&E whole-slide images, extended by CellViT++ with foundation backbones.
Family of transformer-based DNA language models using BPE tokenization and BigBird sparse attention to reach context lengths up to 36,000 base pairs.
Multimodal aging clock predicting chronological age from DNA methylation or bulk RNA-seq, with a frozen backbone reused for disease target discovery.
Sequence-pair interaction classifier fine-tuned from ProtBERT-BFD, trained against synthetic negatives generated by BLOSUM62-guided mutation.
Vision transformer for electrocardiograms that reads the printed 12-lead ECG as an image, enabling data-efficient diagnosis from few labeled examples.
Predicts 12 types of RNA modification sites from sequence, fine-tuning DNABERT representations fused with CNN-encoded sequence features.