Development and Verification of a Combined Diagnostic Model for Sarcopenia with Random Forest and Artificial Neural Network.

ABSTRACT:

Background

Sarcopenia is a chronic disease characterized by an age-related decline in skeletal muscle mass and function, and diagnosis is challenging owing to the lack of a clear "gold standard" assessment method.

Objective

This study is aimed at combining random forest (RF) and artificial neural network (ANN) methods to screen key potential biomarkers and establish an early sarcopenia diagnostic model.

Methods

Three gene expression datasets were downloaded and merged by searching the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in the merged dataset were identified by R software and subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Afterward, the STRING database was employed for in

SUBMITTER: Lin S 

PROVIDER: S-EPMC9427323 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature