Abstract
Multiple sclerosis disease is a main cause of non-traumatic disabilities and one of the most common neurological disorders in young adults over many countries. In this work, we introduce a survey study of the utilization of machine learning methods in Multiple Sclerosis early genetic disease detection methods incorporating Microarray data analysis and Single Nucleotide Polymorphism data analysis and explains in details the machine learning methods used in literature. In addition, this study demonstrates the future trends of Next Generation Sequencing data analysis in disease detection and sample datasets of each genetic detection method was included .in addition, the challenges facing genetic disease detection were elaborated.
Highlights
Multiple Sclerosis (MS) is a demyelination disease, that is, the immune system attacks the myelin sheath causing fatal damages to the nerve cells in human Central Nervous System (CNS) and fatal physical disabilities including partial or total blindness, double vision, muscle weakness, motor disabilities in addition to mental, and sometimes psychiatric impacts[1][2]
Results have shown that Octopus-toolkit can deliver the result faster than Galaxy or GenePattern products provided that a computer that is capable of Next Generation Sequences (NGS) analysis is used
Dataset Description Gene expression profiles dataset obtained from naive CD4+ T cells consists of 54675 probes and 113 samples: 73 MS cases and 40 controls by the European Bioinformatic Institute EGEOD 13732 microarray – used as training dataset. gene expression profiles dataset obtained from t cells of 20 samples: 10 ms cases, 10 controls and 54675 probes by the european bioinformatic institute e-geod-43592 microarray) – used as validation dataset Gene expression profiles dataset of 26 multiple sclerosis cases and 18 controls
Summary
Multiple Sclerosis (MS) is a demyelination disease, that is, the immune system attacks the myelin sheath causing fatal damages to the nerve cells in human Central Nervous System (CNS) and fatal physical disabilities including partial or total blindness, double vision, muscle weakness, motor disabilities in addition to mental, and sometimes psychiatric impacts[1][2]. Machine Learning has played a significant role in MS detection by analyzing Magnetic Resonance Imaging (MRI) data, primarily by detecting the existing lesions caused by the MS in the central nervous system [14][15][16][17]. Due to serious tissue damage, the abnormal white matter areas of the central nervous system appear normal in MRI images [23] Another detection method for MS that have machine learning methods is analyzing different wavelet transform methods of MRI images such as two dimensional, biorthogonal, and Haar wavelet transform. This survey introduces how machine learning methods were used in the early genetic detection of this autoimmune disease [33][34]
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