Hailin Hu

ACME improves peptide-MHC class I binding prediction using attention-based deep learning, outperforming existing methods and providing interpretable insights.

This study introduces RiboRL, a deep reinforcement learning framework for predicting ribosome densities on transcripts, showing improved performance over existing methods.

DeepHINT predicts HIV-1 integration sites and explains their selection using an attention-based deep learning framework, outperforming conventional methods.

GEM reconstructs 3D chromatin structures from Hi-C data using manifold learning and conformational energy, outperforming existing methods and recovering long-range interactions.

TITER predicts translation initiation sites using deep learning on QTI-seq data, outperforming existing methods and identifying sequence signatures for different TIS codons.

ROSE predicts ribosome stalling from ribosome profiling data, showing better performance than conventional models and revealing regulatory factors and functional interactions.