Computational Prediction of Aging Genes in Human

Aging is a complex process associated with a number of molecular events at multidimensional levels. Understanding the characteristics of aging is important for elaborating the molecular mechanism of many diseases such as Alzheimer. In this paper, we systematically analyzed topological features of proteins encoded by human aging genes versus those encoded by non-aging genes in protein-protein interaction (PPI) network and found that they are characterized by several topological features such as higher in degrees. In gene expression pattern, we found that aging genes tend to have higher co-expression coefficients with other genes than that of non-aging genes in the gene expression profile. Based on these computational features, an algorithm was developed to detect aging genes genome wide. With a probability score of 0.85, 168 novel aging genes were predicted. Evidence supporting our prediction can be found.