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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1729–1752 of 2336 models
Patient-level single-cell foundation model that condenses a donor's scRNA-seq profile into one 288-dimensional embedding for disease cohort search.
Molecular property prediction from 3D structure for assays with dozens of labels, pretrained by extreme denoising plus DFT and LLM auxiliary labels.
RNA foundation models that learn their own character-level tokenization instead of fixed nucleotide or k-mer vocabularies. Sizes run 8M to 650M.
Imaging-genetics foundation model pairing SNP genotypes with brain-MRI phenotypes by contrastive learning to surface many-to-many associations.
Metagenomic read binning from tetranucleotide k-mer profiles, using a contrastive two-layer encoder trained on split halves of unlabeled reads.
Protein inverse folding as a generative Markov bridge, refining a structure-derived sequence prior with a frozen protein language model.
Cell microscopy foundation model with a 1.9-billion-parameter masked autoencoder producing embeddings that stay consistent across screening batches.
Post-translational modification prediction from sequence and 3D structure, quantizing each residue's micro-environment into a per-PTM discrete token.
Molecular scaffold optimization that grafts generated fragments onto a lead compound, guided by Bayesian search in a conditional VAE latent space.
Peptide-MHC class I immunogenicity prediction fusing sequence, predicted structure, and biochemical properties for vaccine and neoantigen design.
Protein subcellular localization from sequence, returning both a text label and a synthetic fluorescence image of the protein inside a given nucleus.
Cell phenotyping model for spatial proteomics using a language-informed vision transformer to classify cell types zero-shot across marker panels.
RNA language model built from bidirectional Mamba2 blocks with a flash-attention head, pretrained on 100 million sequences up to 2,048 nucleotides.
Biochemistry-aware inverse folding model that augments backbone geometry with physicochemical point clouds, reaching ~90% sequence recovery on CATH.
Estimation of model accuracy for protein complexes, predicting per-residue lDDT from Voronoi contact areas and contact-surface orientation features.
Hi-C contact map super-resolution that adds interaction frequencies imputed from DNase-seq accessibility so one cell line's model transfers to others.
DNA methylation foundation model over 49,156 array CpG sites. Imputes missing values, embeds samples, and predicts epigenetic age and disease risk.
Whole-genome somatic copy-number aberration prediction from bulk RNA-seq alone, with one pan-cancer model covering 33 tumor types.
Phase separation prediction from sequence alone, pairing a protein language model with MD-trained conformational features to score every residue.
Single-cell RNA-seq foundation model pretrained only on malignant cells, for zero-shot batch integration and drug response prediction in tumors.
Protein interface prediction from sequence alone, swapping hand-crafted features for frozen ProtT5-XL embeddings that hold up on remote homologs.
Poly(A)-tail length change predicted from mRNA 3' UTR sequence in maturing oocytes, scoring how single-nucleotide variants disrupt tail lengthening.
Protein-ligand binding affinity prediction that fine-tunes ESM-2 and ChemBERTa-2 into a shared space where cosine similarity is the predicted pKd.