All Competitors

Bayesian deep generative model that integrates gene regulatory networks into RNA velocity inference, enabling cell fate mapping and in silico perturbation of transcription factors.

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.

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

A foundation model for global 3D genome architecture that uses masked locus modeling over genome-wide contact profiles to represent chromosome-scale organization.

A multimodal single-cell foundation model with a multiway Transformer that jointly models scRNA-seq and scATAC-seq, including RNA-only, ATAC-only, and paired inputs.

A short-context masked DNA language model trained on curated regulatory sequences with a motif-discovery regularizer for zero-shot TF motif recovery and variant effect prediction.

A Mixture-of-Experts generative model that turns DNA sequence plus cell-type ATAC-seq into unified epigenomic, transcriptomic, and 3D chromatin profiles, generalizing to unseen cell types.

Predicts single-cell-resolution scRNA-seq coverage and scATAC-seq insertion profiles directly from DNA sequence by adapting the Borzoi predictor with a cell-specific decoder.

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

A graph-signal-processing foundation model that embeds gene regulatory network structure directly into its attention mechanism for parameter-efficient single-cell transcriptomics.

Google DeepMind model that predicts thousands of functional genomic tracks at single base-pair resolution from megabase-scale DNA sequences. Open-sourced with public API access in January 2026.

Deep learning model predicting single-cell read counts from DNA sequence features and cell transition graphs to identify transcriptional regulators.

BERT-based model pretrained on 15-state ROADMAP chromatin annotations across 127 human cell types to uncover chromatin-state motifs and predict gene expression.

Explainable sequence model for transcription initiation that identifies the minimal set of sequence rules governing human promoter activity at base-pair resolution.

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

Machine learning framework for inferring cell-type-specific gene regulatory networks from single-cell multi-omics data and simulating transcription factor perturbations in silico.

Self-attention and densely connected convolutional model for predicting gene expression from histone modifications, with transfer learning for cross-cell-line generalization across 56 REMC cell types.

Transformer-based model for predicting gene expression from histone modifications, incorporating 3D chromatin interaction data and large genomic windows to capture distal regulatory effects.

Multi-modal self-supervised model pre-trained on regulatory genome sequences and transcription factor binding matrices for cell-type-specific regulatory prediction.

Updated Basenji architecture enabling cross-species regulatory sequence activity prediction, trained jointly on human and mouse genomes with improved generalization.

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

Attention-based deep learning model that predicts gene expression from histone modification signals across 56 cell types with interpretable attention scores.

Deep convolutional neural network that learns the regulatory code of DNA accessibility from DNase-seq data across 164 cell types, enabling variant effect prediction at cis-regulatory elements.