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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 121–144 of 423 filtered models
Pan-cancer multi-omic foundation model encoding CpG-island DNA methylation and RNA-seq for zero-shot cancer classification and mutation prediction.
Mutation effect prediction at protein–DNA and protein–RNA interfaces, combining frozen ESM-2 embeddings with an edge-aware atomic graph network.
Bidirectional state-space (Mamba-2) genomic model for ultra-long extrachromosomal circular DNA, scaling linearly with sequence length.
Conditional codon language model with 150M parameters that generates species-optimized coding sequences from a protein and its taxonomic lineage.
Deep learning framework that predicts DNA methylation from genomic sequence across 39 human tissues, with an scRNA-seq variant for unseen cell types.
Gene expression prediction model combining DNA sequence with Hi-C contact maps to capture 3D chromatin looping behind cell-type-specific expression.
Cross-species-pretrained CNN that predicts single-CpG DNA methylation from genomic sequence and interprets the cis-regulatory motifs that govern it.
Multimodal foundation model that distills Evo 2 into a compact encoder guided by Hi-C data, predicting cell-type-specific 3D genome architecture.
Hierarchical DNA foundation model that co-trains a dynamic token-merging tokenizer with latent Transformers to match genomic information density.
Histopathology model predicting TP53 mutation status, TP53 RNA expression, and tumour taxonomy from H&E whole-slide images across 32 solid cancers.
Transformer foundation model for single-cell ATAC-seq that embeds both cells and cis-regulatory elements for annotation and batch correction.
Hierarchical transformer with 1.2 billion parameters that predicts personalized gene expression from diploid genomes for variant effect prediction.
Multimodal EHR foundation model that fuses polygenic risk scores into a GPT-2-style backbone by cross-attention for zero-shot disease risk prediction.
Transcription factor binding site prediction model that refines a DNABERT-2 backbone with contrastive learning across diverse TFBS types.
Self-supervised models that embed gut metagenomic abundance profiles for robust phenotype prediction in data-limited, cross-cohort settings.
Efficient sequence-to-function transformer for regulatory genomics, matching Borzoi-class models while training in about a day on a single GPU.
Tabular foundation model adapted for extreme feature counts, enabling in-context prediction on wide omics tables with tens of thousands of features.
Genomic language model that flags long reads chimerized by whole-genome amplification so they are not called as structural variants.
Nucleic acid inverse-folding network that designs RNA sequences for a target 3D backbone and predicts protein-DNA binding specificity.
Scientific multitask language model whose byte-level genome modeling beats Evo 7B on DNA perplexity and mutation-effect prediction at 1.5B params.
Multi-label classifier over ESM-2 embeddings that separates DNA-binding, RNA-binding, and dual-binding proteins using label-aware attention.
DNA sequence embedding model that approximates edit distance via contrastive fine-tuning of DNABERT-2, improving similar-sequence search accuracy.