Ke Ding

NucEL improves genomic representation learning by using ELECTRA-style pre-training with single-nucleotide tokenization, achieving state-of-the-art performance on multiple tasks.

The paper evaluates and optimizes sequence-based gene regulatory deep learning models using a DREAM Challenge dataset, showing improvements through architectural and training strategy refinements.

CRMnet is a deep learning model that predicts gene expression from regulatory sequences in yeast, achieving high accuracy and identifying transcription factor binding sites through model interpretation.