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(fēng)，復(fù)旦大學(xué)計算機(jī)科學(xué)技術(shù)學(xué)院副教授，博士生生導(dǎo)師。香港城市大學(xué)博士（2003）,日本京都大學(xué)博士后（2004-2008）,日本學(xué)術(shù)振興會邀請訪問學(xué)者（JSPS Invitation Fellowship 2012）,美國伊利諾伊 大學(xué)香檳分校訪問學(xué)者（2013-2014）,日本京都大學(xué)訪問副教授（2016）。主要研究方向為生物信息學(xué)、信息檢索和數(shù)據(jù)挖掘。在相關(guān)領(lǐng)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(xué)基金面上項目：大規(guī)模生物醫(yī)學(xué)文獻(xiàn)醫(yī)學(xué)主題詞的高精度自動標(biāo)注研究（61572139）和MHC II類分子親和肽的高精度預(yù)測研究 （61170097已結(jié)題）；一項國家自然科學(xué)基金青年項目：基于信息融合的生物醫(yī)學(xué)文本高性能聚類研究（60903076已結(jié)題）。BIBM2014-2017、InCoB2012-2017、GIW2015-2017、APBC2014-2018等生物信息學(xué)國際會議程序委 員會委員。中國計算機(jī)學(xué)會計算機(jī)術(shù)語審定工作委員會委員（2010-2015），負(fù)責(zé)生物信息學(xué)名詞審定。中國人工智能學(xué)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(jī)學(xué)會生物信息專業(yè)委員會初始委員，中國中 文信息處理學(xué)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(yùn)籌學(xué)會計算系統(tǒng)生物學(xué)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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