Yitan Zhu

This paper develops a spatio-temporal analysis framework to uncover coupled mutagenic mechanisms in radiation-induced genomic instability using whole-genome sequencing data from irradiated human cells.

This paper introduces MorphoXAI, a human-in-the-loop framework for explaining AI predictions on whole-slide images, showing it provides accurate and interpretable histomorphological explanations at both global and local levels.

This paper addresses data imbalance in drug response prediction by introducing a multi-objective optimization approach using a custom loss function to improve model performance.

The paper introduces TED, an algorithm for constructing tumor phylogenetic trees from mutation profiles, and applies it to cancer datasets to identify early mutation events in tumor evolution.

TCGA-Assembler 2 retrieves and integrates TCGA and CPTAC data, improving functionality for researchers.