报告题目: 演化计算和复杂网络在代谢组学和精准医疗中的应用

报告人：胡婷 博士

报告时间：2018年6月28日（周四）16:00

报告地点：逸夫楼C座314会议室

摘要：

Metabolomics studies use quantitative analyses of metabolites from body fluids or tissues in order to investigate a sequence of cellular processes and biological systems in response to genetic and environmental influences. This promises an immense potential for a better understanding of the pathogenesis of complex diseases. Most conventional metabolomics analysis methods exam one metabolite at a time and may overlook the synergistic effect of combining multiple metabolites. In this article, we proposed a new bioinformatics framework that infers the non-linear synergy among multiple metabolites using a symbolic model and subsequently, identify key metabolites using network analysis. Such a symbolic model is able to represent a complex non-linear relationship among a set of metabolites associated with osteoarthritis (OA) and is automatically learned using an evolutionary algorithm. Applied to the Newfoundland Osteoarthritis Study (NFOAS) dataset, our methodology was able to identify nine key metabolites including some known osteoarthritis-associated metabolites and some novel metabolic markers that have never been reported before. The results demonstrate the effectiveness of our methodology and more importantly, with further investigations, propose new hypotheses that can help better understand the OA disease.

报告人简介：

胡婷博士从2015年一月起担任纽芬兰纪念大学计算机系助理教授。胡婷博士毕业于武汉大学计算数学系本科， 计算机系硕士，并于2010年取得加拿大纽芬兰纪念大学计算机系的博士学位。同年开始美国达特茅斯学院医学院的计算遗传学博后科研。胡婷博士的科研领域是机器学习，演化计算， 计算生物学，生物信息学，以及复杂网络。胡婷博士主持多项加拿大联邦政府和纽芬兰省政府自然科研项目，至今发表学术论文 56 篇，其中包括文章发表于顶级计算生物学和遗传学期刊和会议PLOS Computational Biology, BMC Bioinformatics, Journal of the American Medical Informatics Association, Genetic Epidemiology, Genes and Immunity, BMC Medical Genomics, PSB, 和演化计算期刊和会议 Genetic programing and Evolvable Machines,Artificial Life, GECCO, EuroGP, IEEE CEC。