Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Depression: Overview

Depression: Overview

Depression is a prevalent mental illness marked by persistent sadness and lack of interest in previously enjoyable activities. It can take several forms, including major depression, persistent depressive disorder, and bipolar I and II disorders. Symptoms range from emotional changes like chronic worry to physical changes like sleep disturbances and suicidal thoughts. From a neurobiological perspective, depression is believed to be triggered by abnormalities in the brain's prefrontal cortex,...

Depressive Disorders: Etiology

Depressive Disorders: Etiology

Depressive disorders result from a complex interplay of biological, psychological, and sociocultural factors, each contributing uniquely to the development and persistence of the condition. Understanding these factors provides critical insight into the multifaceted nature of depression.
Biological Factors in Depression
Biological predispositions significantly influence the risk of developing depressive disorders. Genetic studies highlight the role of variations in the serotonin transporter...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Patient Perspectives in Cardiovascular Research: A Qualitative Study of Barriers, Trust, and Strategies for Inclusive Clinical Trials.

Circulation. Population health and outcomes·2026

Same author

Reliability of resting-state photoplethysmogram-based pulse rate variability in lifespan samples: implications for cardiovascular research.

Physiological measurement·2026

Same author

Automating infection indicator extraction in home healthcare through instruction-tuned large language models.

Journal of the American Medical Informatics Association : JAMIA·2026

Same author

Connectome-based predictive modelling of problematic gaming in youth from the ABCD study.

Journal of behavioral addictions·2026

Same author

Cognitive Outcomes in Randomized Controlled Trials of Coronary Artery Bypass Graft Surgery From 2005 to 2025: A Systematic Review.

Journal of the American Heart Association·2026

Same author

How should race be best considered in understanding brain-behavior relationships? Implications for understanding onset of engagement in addictive behaviors and subsequent problems.

Journal of behavioral addictions·2025

Search research articles

Related Experiment Video

Updated: Jun 20, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

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

Brittany Taylor¹, Mollie Hobensack², Stephanie Niño de Rivera¹

¹School of Nursing, Columbia University, New York, NY, United States.

|July 19, 2024

Summary

Machine learning and omics data analysis show promise for objective depression diagnosis. These methods identify biological markers, aiding clinicians, especially nurses, in diagnosing and treating depression more effectively.

Keywords:

depression machine learning mental health nurses omics review

More Related Videos

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

Related Experiment Videos

Last Updated: Jun 20, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

Area of Science:

Biomedical Informatics
Computational Psychiatry
Genetics

Background:

Depression affects over 300 million globally, with diagnosis relying on subjective symptoms.
A shortage of mental health providers necessitates innovative diagnostic approaches.
Omics methods (genomics, transcriptomics, epigenomics, microbiomics) offer objective biological insights into depression.

Purpose of the Study:

To conduct a scoping review of machine learning (ML) applications in omics data analysis for depression identification.
To explore objective, data-driven insights for improving depression diagnosis.

Main Methods:

Scoping review following PRISMA-ScR guidelines.
Searches across 3 databases for relevant literature.
Independent screening and critical appraisal of 15 selected studies.

Main Results:

Identified 15 relevant papers utilizing various omics (genomics, transcriptomics, epigenomics, multiomics, microbiomics).
Common ML methods included random forest, support vector machine, k-nearest neighbor, and artificial neural networks.
Omics methods demonstrated similar performance in identifying depression-associated variants.

Conclusions:

Omics data analysis with ML shows comparable performance in identifying depression-related variants.
All evaluated ML methods performed well in analyzing omics data for depression.
Findings support integrating omics and ML for objective depression assessment and timely clinical intervention by nurses.