Machine learning approach to address sarcopenia on skeletal muscle tissue
الكلمات المفتاحية:
body weight loss، machine learning methods، multi-filter single wrapper systemالملخص
Body weight loss (BWL) or skeletal muscle atrophy (SMA) are normal and common in elderly individuals. The satellite cells are located in the space between the basal lamina and the sarcolemma; often these cells are quiescent. There have been several attempts conducted using machine learning methods to uncover the significant age- related genes. However, these investigations have not described the impact of age on skeletal muscle mass in human. Microarray datasets involve tens of thousands of genes, but just a few genes could lead to uncover the relationship between age and muscle atrophy. The main goal of this paper is to identify age-related genes with high accuracy, the final subset of selected genes was based on Multi-filter single wrapper system (MFSWS). Microarray expression profiles, downloaded from GEO database: GSE1428 was chosen to screen the differentially expressed genes of arm tissue and to compare three age groups of both males and females. Results revealed that our approach is able to identify a subset of genes with high performance of accuracy compared to the existed system (Liu et al, 2013) [27] and MFS (2016) [28].
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