The Chinese University of Hong Kong
Department of Computer Science and Engineering

Joint CSE-BME Seminar

Title: Large-scale Multilabel Learning and its application in Bioinformatics
Date: October 3, 2017 (Tuesday)
Time: 11:00 a.m. - 12:00 n.n.
Venue: Room 121, 1/F, Ho Sin-hang Engineering Building,
The Chinese University of Hong Kong,
Shatin, N.T.
Speaker: Prof. Zhu Shanfeng
Associate Professor
Shanghai Key Lab of Intelligent Information Processing
School of Computer Science
Fudan University, Shanghai, China



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.



Shanfeng Zhu is an associate professor at School of Computer Science and Shanghai Key Lab of Intelligent Information Processing at Fudan University. He was awarded Ph.D. degree in Computer Science in 2003 at City University of Hong Kong. Before joining Fudan University in July 2008, he was a postdoctoral fellow at Bioinformatics Center, Kyoto University. He was a visiting Scholar in UIUC (March 2013-March 2014), and a visiting associate professor in Kyoto University (July 2016-Nov 2016). His research focuses on developing and applying machine learning and data mining methods for Bioinformatics, especially biomedical text mining, immunological informatics, drug discovery and protein function prediction.


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