Tao Jiang

DeltaSplice predicts splicing-altering mutations and alternative splicing from genomic sequences, outperforming existing methods and identifying disease-related variants in neurodevelopmental disorders.

This study introduces PULSE, a deep learning model that predicts pseudouridylation sites and characterizes their sequence features using convolutional neural networks.

MONN predicts non-covalent interactions and binding affinities between compounds and proteins, outperforming existing methods and providing interpretable insights.

OMGS improves genome scaffolding by utilizing multiple optical maps, achieving better contiguity and correctness than existing methods.

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

NeoDTI predicts drug-target interactions by integrating heterogeneous network data, showing improved performance and novel predictions supported by prior studies.

DeBooster improves RBP target prediction from CLIP-seq data by reducing false positives and negatives, and enables new biological insights into RBP functions.

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.