Identification of Diagnostic Markers for Major Depressive Disorder Using Machine Learning Methods.

ABSTRACT:

Background

Major depressive disorder (MDD) is a global health challenge that impacts the quality of patients' lives severely. The disorder can manifest in many forms with different combinations of symptoms, which makes its clinical diagnosis difficult. Robust biomarkers are greatly needed to improve diagnosis and to understand the etiology of the disease. The main purpose of this study was to create a predictive model for MDD diagnosis based on peripheral blood transcriptomes.

Materials and methods

We collected nine RNA expression datasets for MDD patients and healthy samples from the Gene Expression Omnibus database. After a series of quality control and heterogeneity tests, 302 samples from six studies were deemed suitable for the study. R package "MetaOmics" was applied f

SUBMITTER: Zhao S 

PROVIDER: S-EPMC8249859 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature