Title :
A novel approach to breast cancer-related disease genes discovered through variation of density modularity
Author :
Xianjun Shen ; Yang Yi ; Yan Wang ; Xiaohui Chen ; Jincai Yang ; Tingting He
Author_Institution :
Sch. of Comput., Central China Normal Univ., Wuhan, China
Abstract :
Breast cancer is a leading cause of cancer-related deaths in women worldwide. Discovery of breast cancer-related disease genes is becoming very important to researcher and opens a new way to investigate pathogenic mechanism of breast cancer. Many studies have shown that the availability of human genome-wide protein-protein interactions (PPI) provides us with new opportunity for discovering disease-genes by topological features in PPI network. Therefore, it´s a novel idea that map disease genes to proteins and predict disease genes by analyzing modular feature of the human PPI network. In this paper, we propose a Closely Associated Degree (CAD) algorithm based on the variation of density modularity. The CAD algorithm is first tested on the yeast PPI network, and then applied to discover breast cancer-related disease genes. The experimental results show that CAD gets 25 breast cancer-related functional modules that include known disease genes of breast cancer. Further analyzing these functional modules, four breast cancer-related disease genes have been discovered which play a significant role in breast cancer.
Keywords :
associative processing; biochemistry; bioinformatics; biological tissues; cancer; data analysis; data mining; feature extraction; genetics; genomics; medical computing; molecular biophysics; molecular configurations; proteins; topology; CAD algorithm; PPI network modular feature analysis; PPI network topological feature; breast cancer pathogenic mechanism; breast cancer-related disease gene discovery; breast cancer-related functional module; closely associated degree algorithm; density modularity variation; disease gene prediction; disease gene-protein mapping; functional module analysis; human PPI network; human genome-wide protein-protein interaction; yeast PPI network; Breast; Clustering algorithms; Databases; Design automation; Diseases; Prediction algorithms; Proteins; breast cancer; disease genes; external closely associated degree; internal closely associated degree; protein-protein interaction network;
Conference_Titel :
Bioinformatics and Biomedicine (BIBM), 2014 IEEE International Conference on
Conference_Location :
Belfast
DOI :
10.1109/BIBM.2014.6999277
