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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 361–384 of 400 filtered models
Transformer model predicting context-specific epigenomic signals across cell types using DNA sequence and transcription factor activity profiles.
Gut microbiome language model that reads a 16S sample as a sentence of taxa, producing context-sensitive embeddings that transfer across cohorts.
GPT-style DNA foundation model trained on over 200 billion base pairs of mammalian genomes for sequence generation, classification, and regression.
Genomic foundation model built on the Hyena operator, processing DNA at single-nucleotide resolution with context windows up to 1 million tokens.
Multi-species genomic foundation model swapping k-mer tokenization for byte pair encoding, matching Nucleotide Transformer with 21x fewer parameters.
Multimodal aging clock predicting chronological age from DNA methylation or bulk RNA-seq, with a frozen backbone reused for disease target discovery.
Family of transformer-based DNA language models using BPE tokenization and BigBird sparse attention to reach context lengths up to 36,000 base pairs.
Masked DNA language model trained on 800+ species with explicit species conditioning, separating conserved regulatory motifs from background bias.
Masked DNA language model trained on over 800 vertebrate genomes and conditioned on species identity to learn conserved regulatory sequence features.
DNA foundation models from 500M to 2.5B parameters, trained on 3,200+ human genomes and 850 species for variant effect prediction.
In silico saturation mutagenesis in a single forward pass, scoring every substitution in a 2 kb window across 2,002 chromatin profiles.
Gene expression prediction from histone modifications, combining self-attention with dense convolutions and transfer learning across cell types.
Transformer predicting gene expression from histone modifications, using promoter-enhancer Hi-C interactions to capture distal regulatory effects.
DNA language model for interpretable prediction of 4mC, 5hmC, and 6mA methylation sites across species, using multi-scale k-mer BERT encoders.
Genome-scale language model trained on prokaryotic genes and SARS-CoV-2 genomes to model viral evolution and flag emerging variants of concern.
Word2vec-based language model trained on 360 million microbial genes that predicts gene function from genomic context without sequence homology.
Protein-nucleic acid complex structure prediction from sequence, folding protein, DNA and RNA chains in one network with confidence estimates.
Deep learning model predicting DNA methylation regulatory variants at CpG sites in the human brain, fine-mapping psychiatric disorder risk loci.
Motif-oriented DNA pre-training framework that adds motif prediction to an ELECTRA generator-discriminator setup for motif-aware genomic embeddings.
Chromatin-level variant effect prediction from DNA sequence, projecting 21,907 predicted regulatory profiles onto 40 interpretable sequence classes.
Predicts 3D genome architecture directly from DNA sequence across nine scales, from 4-kb contacts up to a 256-Mb whole-chromosome window.
Gut microbiome taxa embeddings that project a 16S V4 ASV table into a shared property space so classifiers transfer between cohorts.
Resolution enhancement for Hi-C contact matrices, reconstructing full-depth 10 kb maps from libraries sequenced at a fraction of the read depth.