I developed a scalable statistical framework for discovering nonlinear molecular interactions and regulatory rewiring in multi-omics data.
Designed and implemented an end-to-end statistical analysis framework for paired multi-omics cancer data to identify nonlinear regulatory changes between healthy and tumor tissues.
The project integrates proteomics and phosphoproteomics from paired hepatocellular carcinoma (HCC) patients and introduces a local dependence approach that moves beyond traditional global correlation methods.
Rather than measuring a single correlation coefficient, the framework estimates local dependence surfaces across the joint distribution of molecular features, enabling the discovery of context-specific signaling rewiring associated with cancer progression.
Traditional biomarker discovery focuses on identifying molecules whose average expression differs between healthy and diseased tissues.
However, many biological processes are driven not by changes in abundance, but by shifts in molecular regulation.
This project aims to detect:
These insights can contribute to: