An Approach for Severity Prediction of Autism Using Machine Learning Effective early diagnosis of autism can have a significant impact on its intervention and treatment. In this paper, an approach is proposed for comprehensively considering genetic factors and environmental factors to predict the severity of autism. According to the Childhood Autism Rating Scale (CARS), a sample set was collected from the autism clinic and a predictive model based on a stacked sparse autoencoder combined with a softmax classifier was constructed. We compared the proposed model with decision trees and support vector machines. Experiments show that the proposed model has a highest accuracy in predicting the severity of autism. Our method can help patients predict their condition and assist doctors in accurate diagnosis.

TexAnASD: Text Analytics for ASD Risk Gene Predictions Autism Spectrum Disorder (ASD) is an extreme neurodevelopmental disease affecting 1 in every 59 children in the United States, and approximately 1% of US population. The clinical traits of the disorder include noticeable deficits in social interactions, language development and in many cases very narrow and repetitive interests and behaviors. ASD is highly heritable genetic disease, but the known causes including the biomarkers associated with ASD form only the tip of the iceberg. Over the past decade extensive research on exome sequences revealed only about one hundred genetic biomarkers with very high confidence. Number of putative ASD causing genes is rapidly growing with the advent of new technologies while researchers are struggling now to assess which genes are the true causing genes. Manual curation of each of the long list of genes is a cumbersome process that requires huge amount of expert workhours, and is therefore expensive. An in silico prediction method can assist the human experts to check only a shortlist of genes that were filtered through a machine learning system. Most of the existing ASD gene prediction algorithms involve highperformance computing platform to analyze largescale genetic data which is counterintuitive to the actual benefit of using an in silico method in the first place. We propose TexAnASD, a text analytics based ASD gene prediction algorithm that only utilizes what we know about each gene that we learn from published literatures. The proposed method outperforms most of the stateoftheart ASD associated gene prediction methods. Moreover, the method offers an inexpensive model than those of the other competing solutions in terms of computational complexity and running time. All source codes, dataset, predictions and functional insights are available at http:ml.cse.ucdenver.eduresearchTexAnASD.