Physiological ageing described by genome-wide expression trends

Biomedical research uses gene expression in order to understand biological processes and diseases. The level of gene expression is subject to change during the lifetime of an individual and this deregulation can reflect ageing-associated disorders and decline in physical ability. By studying the genome-wide expression trends of the human genes, underlying age-associated molecular processes may be identified and further understood. To perform an adequate analysis, different data mining and pattern recognition concepts are to be applied in the data processing. This paper describes a step-by-step methodology developed to perform longitudinal gene expression microarray dataset analysis in order to identify such age-associated expression trends and determine their biological functionality. The analysis was performed on healthy skeletal muscle tissue samples and validated on several other tissues obtained from non chronically ill individuals. Significant ageing-associated gene clusters were identified as a result of the study.