The Information Filtering of Gene Network for Chronic Diseases: Social Network Perspective

Abstract

Web and mobile platforms have provided an environment of technical cooperation through technical development and the diffusion of related devices. Large-scale data sets have been available to analyze web interaction and data analysis. Particularly, large-scale data make us learn new patterns and insight into several research fields. For healthcare field, most chronic diseases are caused by environmental and genetic factors (Van der Laan et al., 2003). The relationship between environmental exposure and gene factors is crucial regarding disease etiology (Swift et al., 2004). For example, Tobacco is considered one of the biggest environmental factors responsible for many diseases each year. Schwartz and Collins (2007) discussed the importance of gene and environment factor correlation in human diseases. Thomas (2010) published a review of different approaches on gene-environment association studies attempting to explain some of the most complex diseases. Although previous studies have studied chronicle diseases with their causes one by one, those studies do not show integrated relationships between various diseases and their related human genes. Therefore, this study investigates the gene-disease relationships which are affected by tobacco and is able to find new association links with social network analysis and other mining techniques.