Early antidepressant treatment response prediction in major depression using clinical and TPH2 DNA methylation features based on machine learning approaches.

ABSTRACT:

Objective

To identify DNA methylation and clinical features, and to construct machine learning classifiers to assign the patients with major depressive disorder (MDD) into responders and non-responders after a 2-week treatment into responders and non-responders.

Method

Han Chinese patients (291 in total) with MDD comprised the study population. Datasets contained demographic information, environment stress factors, and the methylation levels of 38 methylated sites of tryptophan hydroxylase 2 (TPH2) genes in peripheral blood samples. Recursive Feature Elimination (RFE) was employed to select features. Five classification algorithms (logistic regression, classification and regression trees, support vector machine, logitboost and random forests) were used to establish the model

SUBMITTER: Chen B 

PROVIDER: S-EPMC10150459 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature