报告人：李国君，山东大学

报告时间：2018年12月17日（周一）10点

报告地点：数计学院4号楼225

报告题目：BicGO: a new biclustering algorithm based on global optimization

报告摘要：Recognizing complicated biclusters submerged in large scale datasets (matrix) has been being a highly challenging problem. We introduce a biclustering algorithm BicGO consisting of two separate strategies which can be selectively used by users. The BicGO which was developed based on global optimization can be implemented by iteratively answering if a real number belongs to a given interval. Tested on various simulated datasets in which most complicated and most general trend-preserved biclusters were submerged, BicGO always extracted all the actual bicluters with accuracy 100%, while on real datasets, it also achieved an incredible superiority over all the salient tools compared in this article. To our best knowledge, the BicGO is the first tool capable of identifying any complicated (e.g., constant, shift, scale, shift-scale, order-preserved, trend-preserved, etc), any shapes (narrow or broad) of biclusters with overlaps allowed. In addition, it is also highly parsimonious in the usage of computing resources.

报告人简介：李国君，山东大学二级教授。1996 年获中科院数学与系统科学研究院博士学位；2004 年被聘为中科院软件科学所研究员； 2004 年被聘为美国佐治亚大学资深研究教授。长期从事图与组合优化、计算机科学和生物信息学研究。获国家自然科学基金重点项目资助 1项；主持国家自然科学面上项目 10 项。证明了以 Chvátal 猜想为代表的四个图论难题；结束了数个可近似性问题的长期争议；刷新了生物信息学领域十多个经典的算法和软件，以第一或通讯作者在生物信息学相关的影响因子超过11的顶级期刊发表论文10篇。