Recent developments in omics studies and artificial intelligence in depression and suicide

Area of Science:

• Neuroscience
• Genomics
• Computational Biology

Background:

• Major depressive disorder (MDD) is a leading cause of disability and is strongly linked to suicide.
• Current understanding of MDD and suicide pathogenesis remains incomplete, hindering effective treatment and prevention.
• There is a critical need for novel molecular pathways, biomarkers, and drug targets for depression and suicide.

Purpose of the Study:

• To provide a systematic overview of recent advancements in transcriptomics, non-coding RNA, and artificial intelligence (AI) in depression and suicide research.
• To discuss emerging research approaches, including spatial transcriptomics.
• To identify challenges and propose future research directions in the field.

Main Methods:

• Review of high-throughput sequencing technologies and whole-genome analysis.
• Analysis of omics data from blood, brain tissue, and animal models.
• Systematic literature review focusing on transcriptomics, non-coding RNA, and AI applications.

Main Results:

• Omics data reveal significant molecular changes, including gene transcripts, epigenomes, and proteins, reflecting disease states.
• Advancements in transcriptomics and non-coding RNA offer insights into MDD and suicide.
• AI is increasingly utilized for analyzing complex biological data in mental health research.

Conclusions:

• Transcriptomics, non-coding RNA, and AI are powerful tools for elucidating the molecular underpinnings of depression and suicide.
• New research methodologies like spatial transcriptomics hold promise for detailed molecular mapping.
• Addressing research challenges and pursuing future studies are crucial for developing effective interventions for MDD and suicide.