Psychiatric disorders are now responsible for the largest proportion of the global burden of
disease, and even more challenges have been seen during the COVID-19 pandemic …

To study mental illness and health, in the past researchers have often broken down their
complexity into individual subsystems (eg, genomics, transcriptomics, proteomics, clinical …

Recent studies have provided promising evidence that neuroimaging data can predict
treatment outcomes for patients with major depressive disorder (MDD). As most of these …

Evidences have shown that both LRFN5 and OLFM4 can regulate neural development and
synaptic function. Recent genome-wide association studies on major depressive disorder …

Although the understanding of the pathophysiology of major depressive disorder (MDD) has
advanced greatly, this has not been translated into improved outcomes. To date, no …

Background Recent genome-wide association studies on major depressive disorder (MDD)
have indicated the involvement of LRFN5 and OLFM4; however, the expression levels and …

Traditional medicine and biomedical sciences are reaching a turning point because of the
constantly growing impact and volume of Big Data. Machine Learning (ML) techniques and …

Major depressive disorder (MDD) is a common mental disorder and is amongst the most
prevalent psychiatric disorders. MDD remains challenging to diagnose and predict its onset …

In a pharmaceutical context, metabolomics is an underexplored area of research.
Nevertheless, its utility in clinical pathology, biomarker discovery, metabolic subtyping, and …

Background: Individuals with major depressive disorder (MDD) and a lifetime history of
attempted suicide demonstrate lower antidepressant response rates than those without a …