All Competitors

Generative pretrained transformer trained on 33 million human cells for single-cell annotation, batch correction, and perturbation prediction.

Transfer learning framework that predicts single-cell gene expression from ~200kb DNA sequences using Enformer embeddings and a lightweight MLP.

Explainable autoencoder for transcriptome analysis that uses SHAP attribution on latent variables to identify critical genes driving gene expression.

Masked DNA language model trained on over 800 vertebrate genomes and conditioned on species identity to learn conserved regulatory sequence features.

DNA foundation models from 500M to 2.5B parameters, trained on 3,200+ human genomes and 850 species for variant effect prediction.

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.

Genome-scale language model trained on prokaryotic genes and SARS-CoV-2 genomes to model viral evolution and flag emerging variants of concern.

Transformer that predicts gene expression and epigenomic signals from 200kb of DNA sequence, capturing distal enhancers up to 100kb from a promoter.

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.

Attention-based model predicting gene expression from histone modification signals across 56 cell types, with interpretable attention scores.