De-Shuang Huang

The paper introduces a two-stage framework for gene expression prediction from cis-regulatory elements, showing improved performance across human cell types and tissues.

CAMEX integrates, aligns, and annotates multi-species scRNA-seq data using a GNN, outperforming existing methods and enabling species-specific cell type detection.

This paper introduces ScPGE, a framework predicting gene expression from cis-regulatory elements using convolutional neural networks and transformers, showing improved accuracy in identifying enhancer-gene interactions.

STAX is a multi-task learning platform for spatial omics that addresses multiple analytical challenges, offering superior performance and biological insights.

The paper proposes two computational methods to predict and characterize cell-type-specific and shared TF binding sites using XGBoost and CNN, showing improved performance over existing methods.

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.