Predicting autism spectrum disorder from associative genetic markers of phenotypic groups using machine learning

Machine learning is a discipline of artificial intelligence, geared towards the development of various critical applications. Due to its high precision, it is widely adopted in the process of extracting useful hidden patterns and valuable insights from complex data structures. Data extracted from the real-time environment might contain some irrelevant information. The presence of noise in the data degrades the model performance. Gene expression is an important source, carries the genetic information of species. Gene expression pattern reveals the significant relationship between genes associated with several diseases. But due to irregular molecular interactions and reactions occurs during the transcription process, the gene expressions are minimally affected. It causes a detrimental effect on the identification of biological markers of the diseases. To address this problem, a novel gene selection strategy is proposed to identify the candidate gene biomarkers from the genomic data. Signal to Noise ratio with logistic sigmoid function, Hilbert-Schmidt Independence Criterion Lasso, and regularized genetic algorithm amalgamation finds the optimal features. The proposed system is tested with the microarray gene expression dataset of autism spectrum disorder (ASD), accessed from gene expression omnibus repository. FAM104B, CCNDBP1, H1F0, ZER1 are identified as the candidate biomarkers of ASD. The methodical performance evaluation of the proposed model is examined with widely used machine learning algorithms. The proposed methodology enhanced the prediction rate of ASD and attained an accuracy of 97.62%, outperformed existing methods. Also, this system could act as a significant tool to assist the medical practitioners for accurate ASD diagnosis.