Speaker: Shanfeng Zhu?
???????? ?PhD Associate professor, School of Computer Science,Fudan University.?
Email: zhusf@fudan.edu.cn?
Web：http://datamining-iip.fudan.edu.cn/?
Background:?
1999-2003 Ph.D. Degree, Department of Computer Science, City University of Hong Kong, P.R, China?
1996-1999 M.Sc. Degree, Department of Computer Science, Wuhan University, P.R, China?
1992-1996 B.Sc. Degree, Department of Computer Science, Wuhan University, P.R, China?
Research:
Data Mining, Machine Learning, Information Retrieval, Intelligent Information Processing
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簡介：
??????? 朱山風，復旦大學計算機科學技術學院副教授，博士生生導師。香港城市大學博士（2003）,日本京都大學博士后（2004-2008）,日本學術振興會邀請訪問學者（JSPS Invitation Fellowship 2012）,美國伊利諾伊 大學香檳分校訪問學者（2013-2014）,日本京都大學訪問副教授（2016）。主要研究方向為生物信息學、信息檢索和數(shù)據(jù)挖掘。在相關領域的著名國際期刊和會議如KDD、IJCAI、ISMB、Bioinformatics、NAR、Briefings in Bioinformatics等以第一作者或通訊作者發(fā)表論文50余篇。主持兩項國家自然科學基金面上項目：大規(guī)模生物醫(yī)學文獻醫(yī)學主題詞的高精度自動標注研究（61572139）和MHC II類分子親和肽的高精度預測研究 （61170097已結題）；一項國家自然科學基金青年項目：基于信息融合的生物醫(yī)學文本高性能聚類研究（60903076已結題）。BIBM2014-2017、InCoB2012-2017、GIW2015-2017、APBC2014-2018等生物信息學國際會議程序委 員會委員。中國計算機學會計算機術語審定工作委員會委員（2010-2015），負責生物信息學名詞審定。中國人工智能學會生物信息與人工生命專業(yè)委員會初始委員、中國計算機學會生物信息專業(yè)委員會初始委員，中國中 文信息處理學會醫(yī)療健康與生物信息處理專業(yè)委員會初始委員，中國運籌學會計算系統(tǒng)生物學分會理事。
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Time : 2:30-4:30 pm , Jan. 12（Friday）
Venue: Room 300, SIBS Main Building, Yueyang Road 320
Host： Prof. Sijia Wang
?????? CAS-MPG Partner Institute for Computational Biology
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Title：MeSHLabeler and GOLabeler, Recent Progress in Large-Scale MeSH Indexing and Protein Function Prediction
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Abstract:
????? Many important problems in BioCuration can be modeled as a large scale multi-label learning problem, such as MeSH indexing and protein function prediction. By utilizing learning to rank framework, we have developed MeSHLabeler and DeepMeSH to solve large-scale MeSH indexing problem, and GOLabeler for protein function prediction. 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 (NLM's official solution), for BioASQ3 challenge data with 6000 citations. on the other hand, the empirical results obtained by examining GOLabeler extensively and thoroughly by using large-scale datasets revealed numerous favorable aspects of GOLabeler, including significant performance advantage over state-of-the-art AFP (Automated Function Prediction) methods. According to the initial evaluation of CAFA3 (The Critical Assessment of protein Function Annotation algorithms) in July 2017, GOLabeler achieved the first place in terms of F-max out of around 200 submissions by around 50 labs all over the world.
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???? All are welcome！
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