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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1681–1704 of 2336 models
Structure-based molecule optimization that steers a Bayesian flow network with property gradients over 3D coordinates and atom types at once.
Prompt-guided protein sequence design conditioned on 3D backbones, fold blueprints, and functional tags. 63.21% native sequence recovery on CATH.
Biomedical imaging foundation model that segments, detects, and recognizes structures across nine modalities from natural language prompts.
Predicts protein properties from sequence alone by LoRA fine-tuning ESM-2 and ESM-C backbones, with contact maps biasing attention pooling.
Protein function prediction from 3D structure and sequence, assigning Gene Ontology terms with an ensemble built around rare long-tail terms.
Macrocyclic peptide binder design against protein targets, cyclizing a diffusion backbone generator's positional encoding so it closes rings.
Single-cell foundation model that compresses each expression profile into 64 cross-attention patch tokens for annotation and spatial transfer.
ECG foundation model that learns discrete rhythm tokens from noisy real-world recordings for arrhythmia classification and anomaly detection.
Single-cell foundation model that maps new scRNA-seq datasets onto a metacell coordinate system zero-shot, without batch correction or fine-tuning.
Genomic foundation model with 7B parameters that models prokaryotic DNA, RNA, and protein at single-nucleotide resolution over a 131k-token context.
Single-cell transcriptomic aging clock predicting immune age for CD8+, CD4+ T and NK cells, and transferring to bulk whole-blood RNA-seq.
Tissue-specific histological aging clocks that read biological age and per-organ age gaps from H&E whole-slide images and from blood gene expression.
Peptide-spectrum match rescoring for DDA proteomics, learned end to end from raw MS2 spectra and peptide sequence across 271 million PSMs.
Chemical language model of the human metabolome that generates and ranks candidate structures for unidentified mass spectrometry peaks.
Immune protein structure prediction for TCRs, antibodies, and nanobodies. Adapts ESMFold with LoRA, reaching 1.31 Å RMSD on the CDR3-beta loop.
Missense pathogenicity prediction that folds wild-type and mutant sequences with ESMFold and encodes each structure as a graph autoencoder embedding.
Histopathology model predicting extrachromosomal DNA status from routine H&E slides by first inferring the tumor transcriptome from tile features.
Cell-type-specific gene expression prediction from DNA sequence, mapping Enformer epigenomic features to pseudobulk expression for cell-resolved TWAS.
Sparse autoencoders on ESM-2 embeddings that expose thousands of interpretable features per layer, tied to binding sites, motifs, and domains.
De novo atomic model building from cryo-EM density maps, adapting AlphaFold2 with local attention and a 3D rotary position embedding.
DIA proteomics scoring model that identifies and quantifies peptide precursors, pretrained across 952 mass spectrometry runs instead of one.
Conditional autoregressive genomic language model trained on 13.6M mammalian promoters, scoring promoter variants, including indels, zero-shot.
Cell Painting microscopy foundation model, a channel-agnostic masked autoencoder producing morphological embeddings for zero-shot phenotypic analysis.