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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 49–72 of 423 filtered models
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.
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.