Data-driven Autism Biomarkers Selection by using Signal Processing and Machine Learning Techniques

Abstract

To analyze microarray gene expression data from homogeneous group of children diagnosed with classic autism, a synergy of signal processing and machine learning techniques is proposed. The main focus of the paper is the gene expression preprocessing, which relies on Fractional Fourier Transformation, and the obtained data is further used for biomarker selection using an entropy-based method. This is a crucial step needed to obtain knowledge of the most informative genes (biomarkers) in terms of their discriminative power between the autistic and the control (healthy) group. The relevance of the selected biomarkers is tested using discriminative and generative machine learning classification algorithms. Furthermore, a data-driven approach is used to evaluate the performance of the classifiers by using a set of two performance measures (sensitivity and specificity). The evaluation showed that the model learned by Naive Bayes provides best results. Finally, a reliable biomarkers set is obtained and each gene is analyzed in terms of its chromosomal location and accordingly compared to the critical chromosomes published in the literature.