Identification of Autistic Risk Genes Using Developmental Brain Gene Expression Data

Recently, the serious impairments of ASD cause a series of pending issues to increase a major burden of health and finance globally. In this work, we propose an effective convolutional neural network (CNN) - based model to identify the potential autistic risk genes based on the developmental brain gene expression profiles. Based on the 10-fold cross validations, the simulation experiments demonstrate that the proposed model shows supreme classification results as compared to the other state-of-the-art classifiers. In such an imbalanced dataset, the proposed CNN model achieves the F1-score of 63.07 ± 3.9 and the area under ROC curve of 0.6940. In case study, 70% out of the top-10 predicted risk genes have been confirmed to increase the risk of developing ASD via published literatures. The effectiveness enables our model to serve as a candidate tool for accelerating the identification of autistic genetic abnormalities.