| Abstract |
Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers, with a five-year survival rate below 12%. Its aggressive progression is driven by complex cellular dynamics that conventional computational models oversimplify by imposing tree-like or linear trajectory assumptions. To address this limitation, this project developed a persistent homology-guided pseudotime framework using topological data analysis to detect nonlinear and cyclic gene expression patterns missed by conventional methods. |
