Computational Methods for Predicting Autism Spectrum Disorder from Gene Expression Data

Autism Spectrum Disorder (ASD) is defined as polygenetic developmental and neurobiological disorders that cover a variety of development delays in social interactions. In recent years, computational methods using gene expression data have been proved to be effective in predicting ASD at the early stage. Feature selection methods directly affect the prediction performance of the ASD prognosis methods. With the advances of computational methods and exploding of highdimensional ASD gene expression data, there is a need to examine the performance of different computational techniques in predicting ASD. In this paper, we review and conduct a comparison study of 22 different feature selection methods for predicting ASD from gene expression data. The methods are categorised into traditional methods (14 methods) and network-based methods (8 methods). The experimental results have shown that the network-based methods generally outperform the traditional feature selection methods in all three accuracy measures, including AUC (area under the curve), F1-score, and Matthews Correlation Coefficient.