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Revealing MicroRNA Biomarkers for Alzheimer’s Disease Using Next Generation Sequencing Data

Alzheimer’s disease is recognized as one of the common diseases found among elders, which still has no successful cure. Different technologies such as microarray technology, Sanger sequencing, and Next Generation Sequencing have been used by various researchers for gathering samples. Out of these, Next Generation Sequencing has become more common nowadays, as it is a powerful platform which enables to sequence thousands or millions of DNA molecules simultaneously. A set of samples collected using Next Generation Sequencing technology is used in this study. The initial data set includes 70 samples and 2652 miRNAs. In this study, our goal is to determine the best set of miRNA biomarkers which are highly differentially expressed in Alzheimer’s disease. Initially, the data set is preprocessed with the aid of the Galaxy tool and python programming language. Significance value, fold change and area under curve analysis are the statistical methods which are used in this study. Random Forest algorithm and Principal Component Analysis are used for selecting the best set of biomarkers out of the data set obtained at the end of statistical analysis. Using the statistical methods, followed by machine learning techniques, we establish 25 microRNAs as biomarkers for Alzheimer’s disease. Furthermore, we provide an analysis of the selected 25 microRNAs with area under the receiver operating curve and classification algorithms.