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

Major depressive disorder (MDD) is the most prevalent and severe form of mental illness and is significantly linked to suicide. At present, addressing the treatment and prevention of depression and suicide poses significant challenges, largely due to the remaining uncertainties surrounding their pathogenesis. Thus, there is an urgent need to find new molecular pathways, as well as effective biomarkers and drug targets, to provide effective diagnosis, prognosis, and treatments for depression and suicide. Recent advancements in high-throughput sequencing technology and whole-genome analysis have enabled the collection of extensive omics data from blood samples, human autopsy brain tissue, and various animal models. This data captures significant molecular-level changes, including alterations in gene transcripts, epigenomes, and proteins, effectively reflecting the biological state of the disease. This review provides a systematic overview of advancements in transcriptomics, non-coding RNA, and AI related to depression and suicide. It discusses new research approaches, such as spatial transcriptomics, addresses challenges connected to various research materials and methodologies, and proposes avenues for future studies.