Leveraging the enrichment analysis from a genome-wide association study against epilepsy-focusing on the role of tryptophan catabolites pathway in patients with drug-resistant epilepsy.

Background: Drug-resistant epilepsy (DRE) is a chronic neurological disorder with somatic impacts and an increased risk of psychiatric comorbidities and cognitive impairment. Previous studies suggested that genomic variants could contribute to the high interindividual variability in epilepsy and in its treatment response, but it remains unclear. Here, we aimed to perform genome-wide association study (GWAS), leverage the enrichment analysis of the genomic variants, and provide the potential molecular signature profiles.

Predicting the Treatment Outcomes of Antidepressants Using a Deep Neural Network of Deep Learning in Drug-Naïve Major Depressive Patients.

Predicting the treatment response to antidepressants by pretreatment features would be useful, as up to 70-90% of patients with major depressive disorder (MDD) do not respond to treatment as expected. Therefore, we aim to establish a deep neural network (DNN) model of deep learning to predict the treatment outcomes of antidepressants in drug-naïve and first-diagnosis MDD patients during severe depressive stage using different domains of signature profiles of clinical features, peripheral biochemistry, psychosocial factors, and genetic polymorphisms. The multilayer feedforward neural network containing two hidden layers was applied to build models with tenfold cross-validation.