Identification of Diagnostic Biomarkers in Post-Traumatic Stress Disorder (PTSD) through Machine Learning

PTSD, a disorder that is often misdiagnosed due to its complicated clinical and biochemical components. However, it may be diagnosed and treated using machine learning (ML) and big data analysis techniques. This approach has the potential to lessen the burden on people worldwide by improving clinical outcomes and reducing the expenses related to long-term disabilities. In the present study, we aim to identify the potential diagnostic biomarker of US marine PTSD patients using network biology, gene expression data analysis, machine learning, and functional enrichment. Our results identify the potential involvement of four genes, namely CMPK2, DDX60, IFI44L, XAF1 as putative GBM associated PTSD biomarkers.