All Competitors

Discrete diffusion model that designs regulatory DNA with tunable cell-type-specific activity and learns activity-predictive representations.

DNA language model that replaces fixed tokenization with conservation-guided patching, letting models up to 10x smaller match top 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 detects positive selection via haplotype-wise attention.

DNA foundation model using masked discrete diffusion to unify bidirectional sequence understanding and de novo generation in one architecture.

Large language model trained on functional genomics data to prioritize novel therapeutic targets from genome-wide CRISPR knockout screens.

Foundation model for 3D genome architecture, using masked locus modeling over genome-wide contact profiles to capture chromosome-scale organization.

Multimodal deep learning model that predicts protein-mediated chromatin contact maps and loops de novo from protein-binding profiles and sequence.

Plant genomic foundation models from 0.1B to 1B parameters, pretrained on 43 phylogenetically diverse plant genomes for variant effect prediction.

Prime editing efficiency prediction from pegRNA sequence, with every biochemical step of the editing mechanism modeled as its own learned rate.

Genomic foundation model training framework whose joint-embedding predictive objective learns functional representations of masked DNA, not tokens.

BERT-style language model for somatic mutations, pretrained on cancer sequencing from 210,000+ patients for tumor subtyping and therapy response.

Transformer that infers whole-genome DNA methylation from gene expression, generalizing zero-shot to unmeasured CpG sites and unseen samples.

Genomic language models fine-tuned to detect and classify antibiotic resistance genes, catching divergent ARGs that reference alignment misses.

Liquid-biopsy deep learning model that infers transcriptome-wide tumor gene expression from standard-depth cell-free DNA whole-genome sequencing.

Tokenizer-free genomic foundation model that adaptively chunks raw nucleotides, enabling zero-shot variant fitness and gene essentiality prediction.

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

Masked DNA language model for regulatory genomics with a motif-discovery regularizer for zero-shot TF motif recovery and variant effect prediction.

mRNA language foundation model trained on ~115M protein-coding sequences across the tree of life, unifying mRNA perception and generation.

Genome language model that adds evolutionary-rate prediction to pretraining, improving representations for variant effect and regulatory genomics.

Vision-language DNA model that renders genomic sequence as visual layouts, reading regions up to 450,000 bases with about 20x better token efficiency.

Retrieval-augmented genomic foundation model that gives transformer backbones a hash-based k-mer motif memory for functional genomics tasks.

Family of autoregressive genomic foundation models that reconcile k-mer tokenization with single-nucleotide resolution at contexts up to 98k bp.