All Competitors

Genomics foundation model that represents individual DNA fragments in a learned semantic space for cell-free DNA cancer detection and cell typing.

Genomic foundation model for rice, pretrained on 422 Oryza genomes with a 1 Mbp context window and a 1.25B-parameter mixture-of-experts transformer.

Chromatin-informed foundation model predicting regulatory activity and chromatin state directly from plant genomic sequence in Arabidopsis and rice.

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.

Multimodal single-cell foundation model whose multiway Transformer jointly models scRNA-seq and scATAC-seq from RNA-only, ATAC-only, or paired inputs.

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

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

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

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

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

Predicts single-cell scRNA-seq coverage and scATAC-seq insertion profiles from DNA sequence, adapting the Borzoi trunk with a cell-specific decoder.

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

Single-cell transcriptomics foundation model that encodes gene regulatory network structure into self-attention through graph signal processing.

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

Conditional autoregressive genomic language model trained on 13.6M mammalian promoters, scoring promoter variants, including indels, zero-shot.

Chromatin-state language model pretrained on ROADMAP annotations from 127 human cell types to find chromatin-state motifs and predict gene expression.

Interpretable model of human transcription initiation that decomposes promoter activity into a minimal set of sequence rules at base-pair resolution.

Transformer model predicting context-specific epigenomic signals across cell types using DNA sequence and transcription factor activity profiles.

Gene expression prediction from histone modifications, combining self-attention with dense convolutions and transfer learning across cell types.

Transformer predicting gene expression from histone modifications, using promoter-enhancer Hi-C interactions to capture distal regulatory effects.

Multi-modal self-supervised transformer for regulatory genomics, pre-trained on DNA sequence together with transcription factor binding matrices.

Cross-species convolutional network trained jointly on human and mouse genomes to predict regulatory sequence activity and noncoding variant effects.

Dilated convolutional network that predicts cell-type-specific epigenetic and transcriptional profiles from DNA sequence across mammalian genomes.