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Academy of Mathematics and Systems Science, CAS
Colloquia & Seminars

Speaker:

朱山风 副教授, 复旦大学

Inviter:  
Title:
Large-scale Multilabel Learning and its applications in Bioinformatics
Time & Venue:
2017.11.3 11:00-12:00 N913
Abstract:

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, such as MeSH indexing, drug target interaction prediction and protein function prediction. By utilizing learning to rank framework, we have developed MeSHLabeler and DeepMeSH to solve large-scale MeSH indexing problem, DrugE-Rank to solve drug target interaction prediction 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. In addition, 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. Finally, 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 nearly 200 submissions by around 50 labs all over the world.

 

 

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