Exploring The Underlying Molecular Mechanism Of Mental Illness Based On The Comorbidity Of Mental Disease And Multi-feature Fusion Bayesian Model

Mental disease is one of the major public health problems on a global scale.It involves a wide population,a large number of ages,and a large economic loss,which makes researchers have to pay attention to its pathogenic mechanism and treatment.There are a large number of concomitant diseases in mental illness.In order to make full use of the information related to mental illness,we first conducted the integration of electronic medical record data to extract comorbid diseases of mental illness,and found a significant comorbid relationship between clinical and mental diseases.disease.The comorbidity phenomenon plays a key role in the analysis of molecular mechanisms of disease,genetic cause research and disease management and prevention.By collecting clinical comorbid data,we further obtained 825 pairs of "psychiatric diseasesassociated diseases" comorbidity,and comprehensively integrated the disease-related genes,protein complexes,biological pathways and other diseases.Based on the above data,we then used the network decomposition algorithm to evaluate the genetic relationship through the protein-protein interaction network under the principle of local optimality,and developed a Bayesian model based on multi-feature fusion to regenerate the genes of mental diseases.The result shows that our method can comprehensively utilize disease-gene relationship,disease comorbidity information,gene interaction information and disease pathway information to evaluate disease gene relationship.We also evaluated the racial difference for the genotypes of those new identified causative genes and the gender difference on mental disease,the results showed that some low-frequency mutations in related genes of mental illness have certain gender differences and ethnic differences while others are not.