All Competitors

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

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.

A deep learning framework that predicts locus-specific DNA methylation across 39 human tissues from genomic sequence, with a scRNA-seq-augmented variant for unseen cell types.

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

Multi-language transformer framework using five pre-trained language models to predict DNA methylation (6mA, 4mC, 5hmC) across species.

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

Chromosome-wise explainable autoencoder for dimensionality reduction of DNA methylation data, achieving up to 400-fold compression while enabling interpretable CpG grouping analysis.

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-scale deep biological language model for interpretable prediction of three DNA methylation types (4mC, 5hmC, 6mA) across multiple species using adaptive multi-scale k-mer BERT encoders.

Deep learning model combining CNN and transformer layers to predict DNA methylation regulatory variants in the human brain, enabling fine-mapping of psychiatric disorder risk loci.

BERT-based deep learning model for predicting DNA N6-methyladenine (6mA) modification sites across multiple species, using word2vec encoding and cross-species transfer learning.

Transformer model for imputing single-cell DNA methylation from sparse bisulfite sequencing data, combining axial attention with sliding window self-attention for scalable CpG-level imputation.

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