Machine Learning Analysis of Blood microRNA Data in Major Depression: A Case-Control Study for Biomarker Discovery.

ABSTRACT:

Background

There is a lack of reliable biomarkers for major depressive disorder (MDD) in clinical practice. However, several studies have shown an association between alterations in microRNA levels and MDD, albeit none of them has taken advantage of machine learning (ML).

Method

Supervised and unsupervised ML were applied to blood microRNA expression profiles from a MDD case-control dataset (n = 168) to distinguish between (1) case vs control status, (2) MDD severity levels defined based on the Montgomery-Asberg Depression Rating Scale, and (3) antidepressant responders vs nonresponders.

Results

MDD cases were distinguishable from healthy controls with an area-under-the receiver-operating characteristic curve (AUC) of 0.97 on testing data. High- vs low-severity cases

SUBMITTER: Qi B 

PROVIDER: S-EPMC7689198 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature