Sheng Li

This paper introduces an alignment-free protein classification framework using sequence embeddings to infer evolutionary relationships and functional motifs across diverse protein superfamilies.

This paper introduces a deep learning model to predict glycosyltransferase folds using secondary structure features, identifying novel fold families and improving fold classification.

The paper introduces QSMART, a model that predicts protein kinase inhibitor response by combining statistical and neural network methods, achieving high accuracy and feature explainability.

The paper uses evolutionary analysis of GT-A fold sequences to identify core components and hypervariable loops contributing to catalytic diversity, and develops a machine learning classifier for donor specificity prediction.