Yixiao Chen

MuSHIN improves genome-scale metabolic models by predicting missing reactions using a deep hypergraph learning method, enhancing their predictive accuracy and phenotypic predictions.

DREAM-GNN improves drug repositioning by integrating heterogeneous biomedical data through a dual-route graph neural network framework, outperforming existing methods in recovering repositioning candidates.

This study identifies MoPtc1 and MoPtc2 as type 2C phosphatases that synergistically regulate MAPK pathways in Magnaporthe oryzae during pathogen-host interactions.

The paper introduces CIRCScan, a machine learning framework that predicts circRNA expression using sequence and epigenetic features, identifying H3K79me2 as a key regulator.