A novel machine learning model to predict autism spectrum disorders risk gene

Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders characterized by the difficulties of social interaction and communication skills, limited repetitive interests and behaviors. Diagnosis of ASD at the early stages is very desirable to establish the treatment quickly. From a machine learning view, the ASD risk prediction task is a binary classification of ASD risk genes that is whether a long non-coding RNA (lncRNA) gene causes disease or not. We have developed a machine learning model, trained using brain developmental gene expression data, for the binary classification of ASD risk genes. Our model is composed of two main parts: feature extraction with Haar wavelet transform, discretization methods and classification with Bayes network learning algorithm. We compared the proposed model with various standalone classifier algorithms and hitherto methods on lncRNA gene data. The experimental results confirm the efficiency of our model with sensitivity, area under ROC curve, MCC and F-measure scores of 0.902, 0.839, 0.583 and 0.806, respectively.