报告题目：Large-scale Multilabel Learning and its application in Bioinformatics
报告时间：6月5号下午3：30-4：30
报告地点：办公楼310会议室
报告摘要:
Multi-label learning deals with the classification problems where each instance can be assigned with multiple class labels simultaneously. There are thousands or even more labels in large-scale multi-label learning. Many important problems in bioinformatics can be modeled as a large scale multi-label learning problem. By utilizing learning to rank framework, we have developed MeSHLabeler and DeepMeSH to solve large-scale MeSH indexing problem, and DrugE-Rank to solve drug target interaction prediction problem. DeepMeSH achieved the first place in both BioASQ4 and BioASQ5 challenge, and MeSHLabeler achieved the first place in both BioASQ2 and BioASQ3 challenges. Specifically, DeepMeSH achieved a Micro F-measure of 0.6323, 2% higher than 0.6218 of MeSHLabeler and 12% higher than 0.5637 of MTI, for BioASQ3 challenge data with 6000 citations On the other hand, using benchmark data in DrugBank, experimental results show that DrugE-Rank outperforms competing methods significantly, especially achieving more than 30% improvement in Area under Prediction Recall curve for FDA approved new drugs and FDA experimental drugs.
报告人简介：
朱山风，复旦大学计算机科学技术学院副教授，博士生生导师。香港城市大学博士（2003）,日本京都大学博士后（2004-2008）,日本学术振兴会邀请访问学者（JSPS Invitation Fellowship 2012）,美国伊利诺伊大学香槟分校访问学者（2013-2014）,日本京都大学访问副教授（2016）。主要研究方向为生物信息学、信息检索和数据挖掘。在相关领域的著名国际期刊和会议如KDD、IJCAI、ISMB、Bioinformatics、NAR、Briefings in Bioinformatics等以第一作者或通讯作者发表论文40余篇。BIBM2014-2017、InCoB2012-2017、GIW2015-2017、APBC2014-2018等生物信息学国际会议程序委员会委员。中国计算机学会计算机术语审定工作委员会委员（2010-2015），负责生物信息学名词审定。中国人工智能学会生物信息与人工生命专业委员会初始委员、中国计算机学会生物信息专业委员会初始委员，中国中文信息处理学会医疗健康与生物信息处理专业委员会初始委员，中国运筹学会计算系统生物学分会理事。2014年-2017年连续四次参加BioASQ大规模生物医学文本自动标注国际竞赛中均取得第一名的好成绩，比美国国立图书馆使用软件精度提高约12%。
