Hongli Ma

BATTER predicts bacterial transcript 3' ends by analyzing conserved stem-loop structures, revealing clade-specific properties and premature termination in AMR genes.

RNAsmol is a sequence-based deep learning framework that predicts RNA-small molecule interactions using data perturbation and augmentation, achieving improved performance over existing methods.

The paper uses scRNA-seq and explainable machine learning to analyze how tumor microenvironment cell types affect neoadjuvant chemotherapy outcomes in breast cancer.

Pathformer integrates multi-omics data using a pathway-informed Transformer to improve cancer diagnosis and prognosis prediction.

RNANetMotif identifies sequence-structure RNA network motifs in RNA-protein binding sites using graph theory and validates them with molecular dynamics simulations.