Identifying Depression Through Machine Learning Analysis of Omics Data: Scoping Review.

ABSTRACT:

Background

Depression is one of the most common mental disorders that affects >300 million people worldwide. There is a shortage of providers trained in the provision of mental health care, and the nursing workforce is essential in filling this gap. The diagnosis of depression relies heavily on self-reported symptoms and clinical interviews, which are subject to implicit biases. The omics methods, including genomics, transcriptomics, epigenomics, and microbiomics, are novel methods for identifying the biological underpinnings of depression. Machine learning is used to analyze genomic data that includes large, heterogeneous, and multidimensional data sets.

Objective

This scoping review aims to review the existing literature on machine learning methods for omics data analysis

SUBMITTER: Taylor B 

PROVIDER: S-EPMC11297379 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature