Yu‐An Huang

CSGDA predicts drug responses across biological heterogeneities by addressing distribution shifts using a cell state-guided graph domain adaptation framework.

scKAN improves cell-type annotation and marker gene discovery using Kolmogorov-Arnold Networks, achieving a 6.63% higher macro F1 score than existing methods and identifying potential drug repurposing candidates.

GCNCDA predicts circRNA-disease associations using a graph convolutional network, achieving high accuracy and confirming predictions with literature.