All Competitors

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.

Metagenomic foundation model trained on 9.7 trillion nucleotide tokens for generative therapeutic design across genes, peptides, and microbiomes.

Masked DNA language model with a learnable, adaptive tokenizer that produces context-dependent, variable-length segments instead of fixed k-mers.

Gut microbiome foundation model pretrained on human shotgun metagenomes, learning species-level taxonomic representations for disease prediction.

Annotation-free metagenome embedding pipeline that encodes raw DNA reads with genomic language models and pools them via FAISS k-means clustering.

Multimodal architecture coupling pretrained DNA, RNA, and protein language models with directional cross-attention into one Virtual Cell Embedding.

470M-parameter microbial genome foundation model trained on 234.5B base pairs for multi-scale genomic representation and trait prediction.

Mixture-of-Experts generative model turning DNA sequence plus cell-type ATAC-seq into unified epigenomic, transcriptomic, and 3D chromatin profiles.

Multi-species genomics foundation model spanning representation learning, functional-track prediction, and sequence generation at 1 Mb context.

Pan-viral genomic language model producing fixed genome-level embeddings of viral DNA and RNA, reused across classification tasks without retraining.

Prime editing efficiency prediction that quantifies per-pegRNA uncertainty, pairing a Dirichlet outcome model with conformal coverage guarantees.

Plant genome foundation model pairing a bidirectional Mamba backbone with sparse Mixture-of-Experts, pretrained on 25.4B nucleotides from 42 species.

Prokaryotic genome language model that reads annotated replicons as ordered gene-product descriptors to predict plasmid hosts and gene essentiality.