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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 25–48 of 400 filtered models
B-cell receptor DNA language model pretrained on antibody heavy-chain nucleotide sequences, with embeddings that outperform protein language models.
Mixed-modality metagenomic language model using bidirectional Mamba blocks to embed proteins within 20K tokens of coding and non-coding DNA.
Somatic mutation risk model predicting base-pair mutability from DNA sequence per COSMIC signature, separating passenger hotspots from cancer drivers.
Genomics foundation model that represents individual DNA fragments in a learned semantic space for cell-free DNA cancer detection and cell typing.
Transcription factor binding prediction pairing DNA sequence with base-resolution methylation and DNase accessibility for cell-type-specific calls.
Pan-fungal circRNA prediction from genome sequence and gene annotation alone, ranking candidate backsplice junctions without requiring RNA-seq.
Physics-informed graph neural network predicting metabolite concentrations from gene expression, generalizing zero-shot to unseen metabolites.
Enhancer RNA mapping model that locates eRNA loci genome-wide from DNA sequence and aggregated RNA-seq signal using a CNN-transformer architecture.
Cis-regulatory network reconstruction from DNA sequence, epigenomic tracks, and Hi-C priors, constrained by gene expression. 0.84 zero-shot auROC.
Plant genome foundation model for ab initio gene structure annotation, predicting genes, coding sequences, and exons at single-nucleotide resolution.
Ab initio gene annotation model that predicts gene boundaries and exon-intron structure from raw DNA, generalizing zero-shot to unseen species.
Multimodal foundation model for precision neurology that reconstructs a patient's molecular brain state from blood to predict disease progression.
Gene representation framework fusing DNA, transcript, protein, text, and single-cell embeddings into one latent space that survives missing views.
Genomic language model from Radical Numerics with a 2 Mbp context window, built for zero-shot variant effect prediction and sequence design.
Generative transformer for phylogenetic inference that transduces sets of unaligned molecular sequences directly into Newick-format trees.
Family of ten compact GPT-2 decoder-only DNA language models spanning BPE vocabularies from 16 to 8192 tokens, built for lossless genome compression.
Hallucination framework for de novo nucleic acid design, pairing NA-MPNN sequence proposals with a frozen AlphaFold3 or Protenix structure oracle.
860M-parameter generative single-cell foundation model that jointly represents and generates epigenomic, transcriptomic, and proteomic modalities.
Reasoning LLM that predicts antimicrobial susceptibility of clinical bacterial isolates and supplies mechanistic explanations for each prediction.
Gene regulation model that conditions a pretrained DNA sequence embedding on CpG methylation to capture cell-type and allele-specific regulation.
Fine-tuned Enformer derivative that annotates cis-regulatory elements from DNA sequence, emitting enhancer, promoter, and insulator class labels.
CRISPR off-target prediction model that scores gRNA-DNA specificity from sequence, framing guide-target recognition as cross-modal retrieval.
Transcription factor-DNA binding specificity prediction from sequence, with a physics-derived dual-encoder trained by symmetric contrastive learning.