Leveraging health social networking communities in translational research

作     者：Webster, Yue W. Dow, Ernst R. Koehler, Jacob Gudivada, Ranga C. Palakal, Mathew J. 

作者机构：Eli Lilly & Co Lilly Corp Ctr Discovery Informat Indianapolis IN 46285 USA Indiana Univ Purdue Univ Sch Informat Indianapolis IN USA 

出 版 物：《JOURNAL OF BIOMEDICAL INFORMATICS》 (生物医学情报学杂志)

年 卷 期：2011年第44卷第4期

页      面：536-544页

核心收录：

学科分类：1001[医学-基础医学(可授医学、理学学位)] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

主　　题：Translational research Health social networking Semantic Web Graph algorithm 

摘      要：Health social networking communities are emerging resources for translational research. We have designed and implemented a framework called HyGen, which combines Semantic Web technologies, graph algorithms and user profiling to discover and prioritize novel associations across disciplines. This manuscript focuses on the key strategies developed to overcome the challenges in handling patient-generated content in Health social networking communities. Heuristic and quantitative evaluations were carried out in colorectal cancer. The results demonstrate the potential of our approach to bridge silos and to identify hidden links among clinical observations, drugs, genes and diseases. In Amyotrophic Lateral Sclerosis case studies, HyGen has identified 15 of the 20 published disease genes. Additionally, HyGen has highlighted new candidates for future investigations, as well as a scientifically meaningful connection between riluzole and alcohol abuse. (C) 2011 Elsevier Inc. All rights reserved.