报 告 人：鱼亮 副教授（西安电子科技大学）
报告时间：4月7日（周五下午）15:30～16:00
报告地点：校本部东区计算机大楼402室
邀 请 人：岳晓冬 副教授

报告摘要：
Computational approaches for predicting drug-disease associations by integrating gene expression and biological network provide great insights to the complex relationships among drugs, targets, disease genes and diseases at a system level. Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with a high rate of morbidity and mortality. We provide an integrative framework to predict novel drugs for HCC based on multi-source random walk (PD-MRW). Firstly, based on gene expression and protein interaction network, we construct a gene-gene weighted interaction network (GWIN). Then, based on multi-source random walk in GWIN, we build a drug-drug similarity network. Finally, based on the known drugs for HCC, we score all drugs in the drug-drug similarity network. The robustness of our predictions, their overlap with those reported in Comparative Toxicogenomics Database (CTD) and literatures, and their enriched KEGG pathway demonstrate our approach can effectively identify new drug indications. Specifically, regorafenib (Rank=9 in top-20 list) is proved to be effective in Phase I and II clinical trials of HCC, and the Phase III trial is ongoing. And it has eleven overlapping pathways with HCC with lower p-values. Focusing on a particular disease, we believe our approach is more accurate and possesses better scalability.

报告人简介:
鱼亮，博士，西安电子科技大学计算机学院副教授。分别于2003年、2006年获西安电子科技大学计算机系计算机科学与技术专业工学学士学位、计算机系统结构专业工学硕士学位，2011年获西安电子科技大学计算机系计算机系统结构专业博士学位。2006年3月进入西安电子科技大学计算机学院从事教学和科研工作至今。科研方面，自2007年至今一直从事生物信息学方面的研究工作，目前主要的研究领域包括：生物信息学、数据挖掘和网络医学的研究工作，主要开展了功能模块挖掘、药物靶标预测、药物重定位等研究。