An Expert-guided Hierarchical Graph Attention Network for Post-traumatic Stress Disorder Highly-associative Genetic Biomarkers Identification

Abstract

AbstractPost-traumatic Stress Disorder (PTSD) is a common debilitating mental disorder, that occurs in some individuals following extremely traumatic events. Traditional identification of Genetic Markers (GM) for PTSD is mainly based on a statistical clinical approach by comparing PTSD patients with normal controls. However, these statistical studies present limitations, often generating inconsistent results. Few studies have yet examined thoroughly the role of somatic mutations, PTSD disease pathways and their relationships. Capitalizing on deep learning techniques, we have developed a novel hierarchical graph attention network to identify highly correlational GM (HGMs) of PTSD. The network presents the following novelties: First, both a hierarchical graph structure and a graph attention mechanism have been integrated into a model to develop a graph attention network (GAtN) model. Second, domain-specific knowledge, including somatic mutations, genes, PTSD pathways and their correlations have been incorporated into the graph structures. Third, 12 somatic mutations having high or moderate impacts on proteins or genes have been identified as the potential HGMs for PTSD. Fourth, our study is carefully guided by prominent PTSD literature or clinical experts of the field; any high saliency HGMs generated from our model are further verified by existing PTSD-related authoritative medical journals. Our study illustrates the utility and significance of a hybrid approach, integrating both AI and expert-guided/domain-specific knowledge for thorough identification of biomarkers of PTSD, while building on the nature of convergence and divergence of PTSD pathways. Our expert-guided AI-driven methodology can be extended to other pathological-based HGM identification studies; it will transform the methodology of biomarker identification for different life-threatening diseases to speed up the complex lengthy procedures of new biomarkers identification.