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学术报告——Overview of Computational Systems Medicine

日期:2018-06-26 来源: 作者: 浏览:

报告题目:Overview of Computational Systems Medicine





Precision medicine initiative (PMI)is making it increasingly feasible for physicians to prescribe the right drug, at the right dose, at the right time according to the makeup of their patient’s genome, making genome informed clinical decision support technologies as a reality. Computational systems medicine paves a way to PMI at systems level. In this talk, I will give a brief overview of our research projects on the computational systems bioinformatics, clinical informatics, systems biology and imaging informatics. At molecular level, we will decipher genetics and epigenetic code of Alternative Splicing using a newly developed multi-label and multi-layer deep learning neural network, and then we apply it to TCGA cancer signature discovery. By integrating the signatures from biomedical big data such as genome, imaging and electronic medical records (EMR), we investigate signature-based drug mining approaches to reveal the underlying mechanisms of drug responses, and thus to optimize personalized medicines. At systems level, we will demo how to integrate intracellular, intercellular and tissue level data to model disease progress. The multiscale modeling system established will provide us a critical tool to see how we can manipulate biological conditions to interrupt disease development, which eventually leads to the cure of the diseases. We recently pioneered a new direction called imaging aided surgical design and device optimization. We will demo how to develop a novel biomechanical property-based machine learning approach to prevent Aortic Valve Insufficiency after transcatheter aortic valve replacement (TAVR). Putting these studies together, we hope to draw a big picture of the trend of computational systems medicine.


周教授目前是美国著名的私立大学维克森林大学 (US news排名在23-27)的终身正教授,生物信息与系统生物研究中心主任,生物信息学首席科学家。他目前领导五个实验室的研究工作,整个美国团队共有30多名研究人员。周教授长期从事数据挖掘,生物信息学,系统生物学,和生物医学成像的研究并作出了卓有显著的贡献。他和他的同事们开创了高含量的细胞成像信息学领域以及系统生物学中的多尺度建模领域。他是可以联合基因组学,蛋白质组学,细胞成像, 组织和器官水平成像和建模,以及提高临床诊断和治疗的极少数科学家之一。周教授在大数据挖掘,云计算,人工智能,机器学习,生物信息学,系统生物学,系统医学,生物医学成像,信号处理,图像处理和模式识别,药物靶标预测, 新一代测序数据分析,磷蛋白质组学的信号通路研究, 细胞与细胞的相互作用建模, 癌症干细胞小环境建模, 免疫系统建模,肾组织再生医学系统建模研究,计算机辅助的手术治疗,器官移植的免疫排斥反应的系统建模,以及系统建模指导下的药物治疗系统等有着多年的研究经验。

周教授发表了近200多篇期刊论文,其中不少发表在他研究领域中的顶尖期刊上如ScienceNature Series, Proc. of IEEEIEEE TransactionsCancer ResearchNucleic Acids ResearchBiomaterialsBioinformatics等。基本上所有的期刊都是SCI检索的,总影响因子超过了500,论文在过去的五年被引用次数超过了4000次。另外出版了10本书籍章节,会议论文100多篇,和2本专著。他的个人化治疗的大数据库系统和多次度模型优化的骨再生系统具有很大的市场价值,目前正在申请专利。自2005年来,周教授从美国NIH获得了超过3000万美元的研究经费,其中包括六个NIH R01(PI)、三个 NIH U01(PI)和六个 NIH R01(Co-PI)




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