报告时间:2016年6月8日(周三)下午2点半
报告地点:信息楼三楼310学术报告厅
报 告 人:张朝阳 美国南密西西比大学教授
报告题目:数据挖据和机器学习方法以及在生物信息学和疾病风险预测方面的应用
报告摘要:
The big data analytics depend on the data mining and machine learning methods. In this presentation I will briefly introduce the new advances of big data research in the United Stated of American and then present two data mining and machine learning methodsand their applications in both bioinformatics and healthcare risk prediction. A new Bayesian learning and optimization model (BLOM) was developed in our Data Mining and Bioinformatics Laboratory for inferring gene regulatory networks from time series microarray data. This approach successfully identified hub genes and several important gene regulation relationships in the pathways and also addressed the dynamic change of biological networks in the course of the treatment and recovery of different species. In the second part of the presentation, I will introduce a new multi-label classification method for the health and disease risk prediction based on physical exam records. Experiment results show that the proposed new method results in better performance in terms of accuracy and efficiency. Thetwo methods can be generalized and applied to the data analysis in other areas.
张朝阳博士简介
张朝阳博士现任美国南密西西比大学计算学院终身教授,博士生导师,并于2008年至2014年担任该学院主任。张朝阳博士主要从事数据挖掘,机器学习,计算生物学,生物信息学,医学数据分析,大数据,高性能计算,已及相关的交叉领域的研究。张朝阳教授作为项目主要负责人或共同负责人主持了十七个美国国家及部级纵向科研项目的研究工作。研究机构包括美国国家自然科学基金(多项),国家健康研究院,国防部,美国国土安全局,总计研究经费近五百万美元。张朝阳博士近年来在学术期刊和国际会议论文集发表了60多篇学术论文,并于2005年获得了美国医学物理家协会颁发的年度医学物理期刊的最佳学术论文奖(Sylvia Sorkin Greenfiled Award)。张朝阳博士多次担任美国国家自然科学基金的评审委员, 以及多个国际会议程序委员会副主席和主席。