A diagnostic model for sepsis-induced acute lung injury using a consensus machine learning approach and its therapeutic implications.

ABSTRACT:

Background

A significant proportion of septic patients with acute lung injury (ALI) are recognized late due to the absence of an efficient diagnostic test, leading to the postponed treatments and consequently higher mortality. Identifying diagnostic biomarkers may improve screening to identify septic patients at high risk of ALI earlier and provide the potential effective therapeutic drugs. Machine learning represents a powerful approach for making sense of complex gene expression data to find robust ALI diagnostic biomarkers.

Methods

The datasets were obtained from GEO and ArrayExpress databases. Following quality control and normalization, the datasets (GSE66890, GSE10474 and GSE32707) were merged as the training set, and four machine learning feature selection methods (El

SUBMITTER: Zheng Y 

PROVIDER: S-EPMC10498641 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature