Zhan‐Heng Chen

The paper introduces a deep learning framework called FCNsignal to predict TF-DNA binding signals at base-resolution, outperforming existing methods and demonstrating its ability to locate binding regions and predict motifs.

This paper introduces MeSHHeading2vec, a method that represents MeSH headings as vectors using graph embedding algorithms, showing improved performance in node classification and relationship prediction tasks.

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.