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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1–23 of 23 models
Enhancer RNA mapping model that locates eRNA loci genome-wide from DNA sequence and aggregated RNA-seq signal using a CNN-transformer architecture.
Protein language model that encodes sequences as discrete words from a learned vocabulary for zero-shot function inference and protein design.
Latent diffusion model that designs D-peptide binders against native L-protein targets, generalizing across chirality via axial vector features.
Single-lead ECG foundation model pretrained on 12-lead recordings, weighting contrastive pairs by clinical risk for cardiovascular risk prediction.
Multi-target drug discovery framework pairing a diffusion-transformer generator with evolutionary latent-space search and synthesis-aware scoring.
Antibody language model pretrained only on CDR-H3 loops, giving embeddings for immune repertoire analysis and antibody sequence classification.
Knowledge-enhanced ECG foundation model aligning a ResNet encoder with LLM-generated disease descriptions for zero- and few-shot interpretation.
Single-cell foundation model trained by metric learning to embed scRNA-seq profiles for cell type annotation and similarity search in cell atlases.
Open foundation model for photoplethysmography (PPG), learning morphology-aware waveform representations for cardiovascular and wearable health tasks.
Mamba-based mature RNA foundation model, contrastively trained on splice isoforms and 400+ mammalian species orthologs for mRNA property prediction.
Text-guided MRI synthesis model that generates brain MR sequences and resolutions on demand from routine scans using imaging-metadata prompts.
Deep learning framework predicting equilibrium distributions of molecular systems, enabling efficient ensemble generation and conformation sampling.
Genomic language model trained on metagenomic scaffolds that learns protein co-regulation and function by modeling gene context and operon structure.
Self-supervised 3D CT foundation model that extracts general-purpose tumor representations for cancer imaging biomarker discovery and prognosis.
Diffusion model for synthesizing single-cell RNA-seq data, with guided generation of specific cell types, rare cells, and developmental trajectories.
RNA 3D structure prediction pipeline pairing a transformer (RNAformer) that predicts inter-nucleotide geometries with Rosetta energy minimization.
Discrete diffusion model for protein sequence and MSA generation, enabling controllable de novo design directly in sequence space without structure.
Structure-conditioned graph transformer trained with masked language modeling to learn residue encodings for inverse folding and antibody design.
Joint sequence-structure protein representation framework that fuses ESM-2 language model embeddings with GearNet geometric graph neural networks.
Protein language model pretrained on UniRef90 with masked language modeling and Gene Ontology annotation prediction, at 16 million parameters.