Microarray Gene Subset Selection in Amyotrophic Lateral Sclerosis Classification

摘要

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease causing a progressive loss of motor neurons. The disease prevalence is 5 per 100,000 people. There is no cure and it leads generally to death from respiratory failure in approximately 3-5 years after the first symptoms. The exact causes of the disease are still unknown, however, almost 20% of the known cases have shown gene mutations. The use of gene expression analysis is a powerful tool to discover the most relevant genes in a cellular process, but the high dimensionality of the data makes the feature selection a challenging task. Using a filter method combined with machine learning algorithms, an ALS data set is explored. Bootstrap resampling is used as a way to achieve stability in the whole process.