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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 49–72 of 400 filtered models
Genomic foundation model with 120M parameters that learns adaptive DNA token boundaries by dynamic chunking, not fixed k-mer or byte-pair tokens.
Self-supervised foundation model that embeds cancer genomes from somatic SNVs and copy-number alterations across 33 tumor types for tumor subtyping.
Zebrafish sequence-to-function model predicting cell-type-specific gene expression from DNA sequence across embryonic development.
700M-parameter DNA language model pretrained on the rice pangenome, serving as a reusable base model for crop genomics and molecular breeding.
Contrastive promoter-protein pretraining that aligns bacterial promoters with their encoded proteins to learn regulatory genomics representations.
Mixture-of-Experts genomic foundation model for the human microbiome, with 4.7B parameters pretrained on bacterial, archaeal, and phage genomes.
Promptable DNA language model that generates multi-kilobase plasmid sequences from plain-language component specs, refined with verifiable rewards.
Multimodal framework that detects and localizes DNA lesions from native nanopore signal, built on the damage-aware LesionBERT foundation model.
Multimodal Q-former that fuses DNA sequence, gene context, protein function, and text for zero-shot variant interpretation with a frozen LLM.
Unified bio-language Mixture-of-Experts model spanning DNA, protein sequence and structure, and biological text across eight task families.
Microbiome world model that treats a community as a set of taxa, scoring how well each member fits and predicting community dynamics zero-shot.
DNA language model combining Mamba state-space layers, gated dilated convolutions, and Fourier attention to capture multi-scale regulatory patterns.
Microbiome foundation models that treat microbial community composition as a language, enabling zero- and few-shot transfer across prediction tasks.
Autoregressive DNA foundation model for variant effect prediction, using 6-mer tokenization to match Evo2-7B win rates at far higher throughput.
Phylogeny-aware genome annotation model predicting exons, introns, UTRs and repeats directly from eukaryotic DNA, with no RNA or protein evidence.
Generative multimodal foundation model spanning DNA, RNA, and protein, with any-to-any inference across genome, transcriptome, and proteome.
Autoregressive nucleotide-and-text foundation model generating DNA and RNA sequences from natural-language prompts that name species and function.
Genomic foundation model for rice, pretrained on 422 Oryza genomes with a 1 Mbp context window and a 1.25B-parameter mixture-of-experts transformer.
Generative DNA foundation model trained on 91.7M nucleotide sequences and annotations for species classification and mutation effect prediction.
Single-cell multiomic foundation model that transfers pan-cancer RNA-ATAC regulatory structure into RNA-only tumour datasets via low-rank adapters.
Chromatin-informed foundation model predicting regulatory activity and chromatin state directly from plant genomic sequence in Arabidopsis and rice.
Genomic foundation model that learns DNA representations by predicting masked regions in latent space rather than reconstructing raw nucleotides.
Transformer that classifies tumour types and subtypes from somatic variants in whole-genome and whole-exome data, with auto-downloading checkpoints.
Long-context plant DNA language model, 676M parameters on a Mamba2 backbone, pretrained on 65 angiosperm genomes for cross-species variant annotation.