Identification and validation of a pyroptosis-related signature in identifying active tuberculosis via a deep learning algorithm.

ABSTRACT:

Introduction

Active tuberculosis (ATB), instigated by Mycobacterium tuberculosis (M.tb), rises as a primary instigator of morbidity and mortality within the realm of infectious illnesses. A significant portion of M.tb infections maintain an asymptomatic nature, recognizably termed as latent tuberculosis infections (LTBI). The complexities inherent to its diagnosis significantly hamper the initiatives aimed at its control and eventual eradication.

Methodology

Utilizing the Gene Expression Omnibus (GEO), we procured two dedicated microarray datasets, labeled GSE39940 and GSE37250. The technique of weighted correlation network analysis was employed to discern the co-expression modules from the differentially expressed genes derived from the first dataset, GSE39940. Consequently

SUBMITTER: Liu Y 

PROVIDER: S-EPMC10646574 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature