All Competitors

Conditions a pretrained DNA sequence embedding on CpG methylation to predict gene regulation across cell types and alleles, generalizing zero-shot to imprinting, X-inactivation, and accessibility.

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

Pan-cancer multi-omic BERT-like foundation model that jointly encodes CpG-island DNA methylation and RNA-seq for zero-shot cancer classification and mutation prediction.

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.

A 52.3M-parameter bimodal masked language model that jointly learns representations of bulk RNA-seq expression and DNA methylation for cancer genomics.

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

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

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.