All Competitors

Conditional codon language model with 150M parameters that generates species-optimized coding sequences from a protein and its taxonomic lineage.

Deep learning framework that predicts DNA methylation from genomic sequence across 39 human tissues, with an scRNA-seq variant for unseen cell types.

Gene expression prediction model combining DNA sequence with Hi-C contact maps to capture 3D chromatin looping behind cell-type-specific expression.

Cross-species-pretrained CNN that predicts single-CpG DNA methylation from genomic sequence and interprets the cis-regulatory motifs that govern it.

Multimodal foundation model that distills Evo 2 into a compact encoder guided by Hi-C data, predicting cell-type-specific 3D genome architecture.

Hierarchical DNA foundation model that co-trains a dynamic token-merging tokenizer with latent Transformers to match genomic information density.

Genomics foundation model that unifies sequence-to-function prediction, DNA language modeling, and generative regulatory design in one backbone.

Transformer foundation model for single-cell ATAC-seq that embeds both cells and cis-regulatory elements for annotation and batch correction.

Hierarchical transformer with 1.2 billion parameters that predicts personalized gene expression from diploid genomes for variant effect prediction.

Multimodal EHR foundation model that fuses polygenic risk scores into a GPT-2-style backbone by cross-attention for zero-shot disease risk prediction.

Transcription factor binding site prediction model that refines a DNABERT-2 backbone with contrastive learning across diverse TFBS types.

Self-supervised models that embed gut metagenomic abundance profiles for robust phenotype prediction in data-limited, cross-cohort settings.

Efficient sequence-to-function transformer for regulatory genomics, matching Borzoi-class models while training in about a day on a single GPU.

Tabular foundation model adapted for extreme feature counts, enabling in-context prediction on wide omics tables with tens of thousands of features.

Multimodal scientific foundation model unifying protein, DNA/RNA, and small-molecule structure in one token vocabulary for cross-domain reasoning.

Multimodal foundation model predicting genome-wide binding of chromatin-associated proteins from protein sequence, DNA sequence, and chromatin state.

DNA foundation model that predicts thousands of functional genomic tracks, from expression and splicing to chromatin, at single base-pair resolution.

Bimodal masked language model that jointly encodes bulk RNA-seq expression and DNA methylation into patient-level embeddings for cancer genomics.

Decoder-only transformer that recasts ancestral recombination graph inference as next-token prediction, estimating coalescence times from variation.

Multimodal viral foundation model over nucleotide and protein sequence, built for virus discovery, function annotation, and antibody design.

Open-source framework for building RNA and DNA foundation models, featuring WCED pretraining for transcriptomics and SNP-aware encoding for genomics.

265M-parameter genomic foundation model whose BioToken scheme folds variants and region annotations into the tokens for variant effect prediction.

Genomic language models with disentangled attention, pretrained on prokaryotic and eukaryotic genomes for sequence classification and variant effects.

Single-cell DNA methylation foundation model capturing genome-wide CpG dependencies in whole-genome bisulfite sequencing across tissues and species.