Runtian Gao

SVPG improves structural variant detection using pangenome references and accelerates pangenome graph construction from long-read data.

This paper introduces a physics-based framework for enzyme engineering that combines machine learning and molecular mechanics to predict catalytic rates and selectivity with high accuracy.

This paper introduces SomaSV, a hybrid sequencing framework that improves somatic structural variant detection accuracy while reducing costs by combining long- and short-read data.

HiFiCCL improves ultra-low-coverage genome assembly using high-fidelity long-reads, enhancing structural variant detection and pangenome construction in human and plant datasets.