Abstract
This study leverages microarray data together with statistical and machine learning techniques to investigate the best set of biomarkers in diagnosing multiple sclerosis (MS). In this work to build an automated system to detect MS two phases are approached. The first phase which is of emphasis is to reduce the dimension of features space and select the most discriminative set of features; biomarkers for MS diagnosis. Principal Component Analysis (PCA) was used as a dimension reduction method. Meanwhile, various feature selection methods were used (Fisher score, chi-square, relief, and MRMR). The second phase of this work is the classification phase, where the output of the first phase were assessed. This phase comprises three different classifiers: LDA, SVM, and KNN. Relief feature selection method achieved 100% accuracy with KNN, using 38 differentially expressed genes (DEGs). Out of this set of these DEGs, Key biomarker genes were identified by studying the gene annotation for all. The genes MIF, PTGES3, CYLD, and JAK1 are realized to be associated with immune and neurological functions. Which is of great relevance to MS.
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Ahmed, A.D., Hadhoud, M.M.A., Ghoneim, V.F. (2024). Automatic Detection of Multiple Sclerosis Using Genomic Expression. In: Mosbah, M., et al. Advances in Model and Data Engineering in the Digitalization Era. MEDI 2023. Communications in Computer and Information Science, vol 2071. Springer, Cham. https://doi.org/10.1007/978-3-031-55729-3_12
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