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.

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

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.

BERT-based pre-trained model for DNA sequences using k-mer tokenization. Achieves state-of-the-art performance on promoter, splice site, and transcription factor binding prediction.