Leon Wong

SPRDA predicts disease-associated piRNAs using a matrix completion approach based on structural perturbation, achieving high performance on benchmark datasets.

GNMFLMI predicts lncRNA-miRNA interactions using graph regularized nonnegative matrix factorization, achieving high accuracy and outperforming existing methods.

This paper introduces GAPDA, a graph attention network method for identifying piRNA-disease associations, achieving high AUC in cross-validation and outperforming existing methods.