All Competitors

A 120M-parameter genomic foundation model that learns adaptive DNA token boundaries via H-Net-style dynamic chunking instead of fixed k-mer or byte-pair tokenization.

A zebrafish DNA sequence-to-function model predicting cell-type-specific single-cell expression across 85 cell-type x developmental-timepoint combinations during embryogenesis.

Multimodal deep-learning framework that detects and localizes DNA lesions directly from native nanopore sequencing, built on the damage-aware LesionBERT foundation model.

A multimodal Q-former that fuses DNA sequence, gene context, protein function, and text into a prefix for a frozen LLM, enabling zero-shot genetic variant interpretation.

A pretrained DNA language model combining Mamba state-space layers, gated dilated convolutions, and Fourier-based attention to capture multi-scale genomic regulatory patterns.

An open autoregressive genomic foundation model (0.5B–8B params) with a 6-mer DNA tokenizer, matching Evo2-7B win rates at far higher throughput.

Generative multimodal foundation model that jointly models DNA, RNA, protein, and cellular context across six biological modalities, with SOTA splicing prediction.

An autoregressive nucleotide-and-text foundation model pretrained on ~416B characters from 2,221 eukaryotic species for natural-language-guided conditional generation of DNA and RNA sequences.

Leakage-controlled multimodal model predicting within-species relative protein expression across 385 bacterial species, with transfer to unseen phyla.

A 1.25B-parameter Mixture-of-Experts genomic foundation model for rice, pretrained on 422 Oryza genomes with a 1 Mbp context window.

A supervised, chromatin-informed foundation model that predicts regulatory activity directly from plant genomic sequence in Arabidopsis and rice.

A genomic foundation model that learns DNA representations through joint-embedding prediction in latent space rather than nucleotide reconstruction.

A long-context plant DNA language model (676M params, Mamba2) pretrained on 65 angiosperm genomes for cross-species functional annotation.

Generative discrete-diffusion model that designs regulatory DNA with tunable activity and learns activity-predictive representations rivaling genomic language models.

A DNA language model that replaces fixed tokenization with conservation-guided patching, letting models up to 10x smaller match or beat state-of-the-art genomic benchmarks.

Genomic foundation model for Cypriniformes fish, built on a Mamba-2 state space model with a 32 kb context window for long-range genome modeling.

Self-supervised transformer for population genetics, pretrained on 1000 Genomes data, that learns selection signatures via site- and haplotype-wise attention.

A bidirectional masked discrete diffusion language model over DNA, initialized from Nucleotide Transformer v2, that unifies DNA understanding and generation.

A training framework that grounds genomic foundation models with a joint-embedding predictive objective, learning to predict functional representations of masked DNA rather than reconstructing tokens.

A family of genomic language models fine-tuned from a pretrained DNA language model to detect and classify antibiotic resistance genes beyond reference-database limits.

A tokenizer-free, hierarchical autoregressive genomic foundation model that adaptively chunks raw nucleotides, enabling efficient long-context learning and zero-shot variant and gene predictions.

A structure-aware generative DNA language model pretrained on influenza genomes that forecasts future antigenic variants across regions and subtypes.

An OCR-inspired vision-language model that renders DNA as visual layouts to analyze long genomic sequences with far fewer tokens than sequential tokenizers.

A genome language model that adds evolutionary-rate prediction as a pretraining task, improving representations and variant effect prediction over sequence-only training.