Hong‐Dong Li

stEDGE reconstructs hierarchical spatial domains and transition interfaces in spatial transcriptomics by using edge-guided methods to capture multiscale organization beyond flat partitions.

Unimeth improves DNA methylation detection from nanopore reads across species and modification types by using a transformer framework with multi-phase training.

The paper introduces scMustree, a tree-construction algorithm that improves clustering accuracy and reveals functional associations among cell populations in scRNA-seq data.

This study introduces DISHyper, a hypergraph neural network that integrates annotated gene sets to predict cancer genes, identifying 44 novel ones associated with multiple cancers.

This paper introduces scCAD, a method for identifying rare cell types in scRNA-seq data by iteratively decomposing clusters, and shows it outperforms existing methods across multiple datasets.

This study predicts Alzheimer's disease-associated genes using brain gene networks and identifies causal variants, providing a resource for therapeutic target discovery.

ClusterMine integrates biological knowledge to cluster samples based on gene set expression profiles, improving interpretability of clusters.

iREAD detects intron retention events from RNA-seq data by analyzing read depth and distribution patterns, producing a list of retained introns for further analysis.

GTFtools provides a Python package for analyzing gene models from GTF files, enabling tasks like GC content calculation and gene length extraction.

This paper develops a probabilistic method to model functional relationships at the splice isoform level by integrating heterogeneous genomic data in mice, revealing isoform-specific interactions and functional diversity.