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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1–24 of 201 filtered models
Conditional diffusion model for 5' UTR and UTR-CDS junction design, targeting ribosome load, folding energy and codon adaptation at sampling time.
Long-context RNA foundation model reading whole mRNA transcripts at single-nucleotide resolution, pretrained at a native 10,240 nt context.
Codon language model for mRNA prediction and coding sequence design, steering synonymous codon choice with a swappable host usage prior at inference.
RNA co-design model generating sequence and 3D backbone together, with SE(3) flow matching over coupled base-centered and sugar-centered frames.
Long-context co-folding model for protein, nucleic-acid and ligand assemblies, folding systems up to 16,384 residues on a single GPU.
Self-supervised transformer pretrained on cell-free RNA expression profiles, built as a shared substrate for downstream disease-detection models.
Biomolecular sequence-structure co-design that plans over frozen folding and inverse-folding models with Monte Carlo tree search, training nothing.
Designs RNA and DNA aptamers against protein targets by backpropagating binding and anti-binding objectives through a frozen all-atom predictor.
RNA inverse folding model conditioned on a context-free-grammar parse tree of the target secondary structure, with explicit GC-content control.
m6Am modification site predictor that fuses frozen RNA-FM embeddings, a one-hot BiLSTM, and a typed RNA structure graph by AUC-weighted voting.
RNA foundation model pretrained on 223 eCLIP experiments to predict base-resolution RBP binding, with frozen embeddings that transfer downstream.
miRNA-target interaction model fusing five gated evidence experts, whose frozen representation transfers to siRNA efficacy prediction.
Antisense oligonucleotide activity prediction from sequence, position-specific chemistry, dose, and cell context. Spearman 0.5970 on ASO Atlas.
Single-cell foundation model for maize, pretrained on a 385,675-cell atlas with Gene Ontology priors for cell typing and cross-species transfer.
RNA and single-stranded DNA 3D structure prediction from sequence alone, with no MSA or language-model inputs and roughly 100x cheaper inference.
m6A RNA modification site prediction across the transcriptome, using a CNN-Transformer hybrid to surface unannotated N6-methyladenosine sites.
Pan-fungal circRNA prediction from genome sequence and gene annotation alone, ranking candidate backsplice junctions without requiring RNA-seq.
RNA modification profiling from nanopore direct RNA-seq signal; self-supervised pretraining resolves 11 modification types and extends to new ones.
Atomic-level refinement of RNA 3D structures, using geometric attention networks to guide physics-based Monte Carlo sampling and L-BFGS optimization.
Histopathology-to-molecular alignment model that queries H&E slides with gene-set signatures to predict pathway activity without sequencing.
Enhancer RNA mapping model that locates eRNA loci genome-wide from DNA sequence and aggregated RNA-seq signal using a CNN-transformer architecture.
Supervised variational autoencoder that learns a tissue-aware latent space for bulk RNA-seq, trained on harmonized TCGA, GTEx, and ARCHS4 data.
Generative framework that learns a developmental vector field from scRNA-seq snapshots, coupling flow matching with molecular RNA kinetics.