Identification by genetic algorithm optimized back propagation artificial neural network and validation of a four-gene signature for diagnosis and prognosis of pancreatic cancer.

ABSTRACT:

Background

Although some improvements in the management of pancreatic cancer (PC) have been made, no major breakthroughs in terms of biomarker discovery or effective treatment have emerged. Here, we applied artificial intelligence (AI)-based methods to develop a model to diagnose PC and predict survival outcome.

Methods

Multiple bioinformatics methods, including Limma Package, were performed to identify differentially expressed genes (DEGs) in PC. A Back Propagation (BP) model was constructed, followed by Genetic Algorithm (GA) filtering and verification of its prognosis capacity in the TCGA cohort. Furthermore, we validated the protein expression of the selected DEGs in 92 clinical PC tissues using immunohistochemistry. Finally, intro studies were performed to assess the fu

SUBMITTER: Li Z 

PROVIDER: S-EPMC9668538 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature