A machine learning framework using urinary biomarkers for pancreatic ductal adenocarcinoma prediction with post hoc validation via single-cell transcriptomics.

ABSTRACT: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer with a poor prognosis, thus emphasizing the need for early and accurate diagnostic tools. In this study, we propose a comparative study approach to understand how machine learning (ML) modeling using urinary biomarkers combined with demographic data can predict PDAC. The study also utilized a single-cell RNA sequencing (scRNA-seq) analysis to assess and understand gene expressions of included biomarkers. With inclusion of available biomarkers and incorporation of demographic information, we employed different approaches for preprocessing techniques, normalization approaches, ML techniques, and deep learning (DL) approaches to provide a comprehensive prediction model. The scRNA-seq approach also highlighted the significance o

SUBMITTER: Solomon DD 

PROVIDER: S-EPMC12599323 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature