Yi Feng Pan

This study identifies distinct neurodevelopmental patterns in ASD and introduces LMFGCN, a deep-learning model that improves ASD classification by integrating cortical morphology data.

This paper generates and screens bioactive peptides using deep learning models, demonstrating their potential for therapeutic applications.

The paper normalizes heterogeneous genomic data into images and applies machine learning to classify viral infection stages and detect transmission clusters with high accuracy.

The paper introduces MTDsite, a multi-task deep learning method for predicting protein-binding residues across DNA, RNA, peptide, and carbohydrate, achieving higher accuracy than existing methods.