Yin Hang, Wang ShaoPeng, Zhang Yu-Hang, Cai Yu-Dong, Liu Hailin
Department of Gastroenterology, Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200011, China.
School of Life Sciences, Shanghai University, Shanghai 200444, China.
Biomed Res Int. 2016;2016:7861274. doi: 10.1155/2016/7861274. Epub 2016 Nov 9.
Pancreatic cancer is a serious disease that results in more than thirty thousand deaths around the world per year. To design effective treatments, many investigators have devoted themselves to the study of biological processes and mechanisms underlying this disease. However, it is far from complete. In this study, we tried to extract important gene ontology (GO) terms and KEGG pathways for pancreatic cancer by adopting some existing computational methods. Genes that have been validated to be related to pancreatic cancer and have not been validated were represented by features derived from GO terms and KEGG pathways using the enrichment theory. A popular feature selection method, minimum redundancy maximum relevance, was employed to analyze these features and extract important GO terms and KEGG pathways. An extensive analysis of the obtained GO terms and KEGG pathways was provided to confirm the correlations between them and pancreatic cancer.
胰腺癌是一种严重的疾病，每年在全球导致三万多人死亡。为了设计有效的治疗方法，许多研究人员致力于研究这种疾病背后的生物学过程和机制。然而，这一研究还远未完成。在本研究中，我们尝试通过采用一些现有的计算方法来提取胰腺癌的重要基因本体（GO）术语和KEGG通路。已被验证与胰腺癌相关以及未经验证的基因，通过使用富集理论从GO术语和KEGG通路衍生的特征来表示。采用一种流行的特征选择方法——最小冗余最大相关法，来分析这些特征并提取重要的GO术语和KEGG通路。对获得的GO术语和KEGG通路进行了广泛分析，以确认它们与胰腺癌之间的相关性。
