In-silico identification of therapeutic targets in pancreatic ductal adenocarcinoma using WGCNA and Trader.

ABSTRACT: Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy, accounting for over 90% of pancreatic cancers, and is characterized by limited treatment options and poor survival rates. Systems biology provides in-depth insights into the molecular mechanisms of PDAC. In this context, novel algorithms and comprehensive strategies are essential for advancing the identification of critical network nodes and therapeutic targets within disease-related protein-protein interaction networks. This study employed a comprehensive computational strategy using the metaheuristic algorithm Trader to enhance the identification of potential therapeutic targets. Analysis of the expression data from the PDAC dataset (GSE132956) involved co-expression analysis and clustering of differentially expre

SUBMITTER: Yavari P 

PROVIDER: S-EPMC11488225 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature