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    Minimum Information Loss Based Multi-kernel Learning for Flagellar Protein Recognition in Trypanosoma Brucei

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    Type
    Conference Paper
    Authors
    Wang, Jim Jing-Yan
    Gao, Xin cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Computational Bioscience Research Center (CBRC)
    Date
    2014-12
    Permanent link to this record
    http://hdl.handle.net/10754/565847
    
    Metadata
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    Abstract
    Trypanosma brucei (T. Brucei) is an important pathogen agent of African trypanosomiasis. The flagellum is an essential and multifunctional organelle of T. Brucei, thus it is very important to recognize the flagellar proteins from T. Brucei proteins for the purposes of both biological research and drug design. In this paper, we investigate computationally recognizing flagellar proteins in T. Brucei by pattern recognition methods. It is argued that an optimal decision function can be obtained as the difference of probability functions of flagella protein and the non-flagellar protein for the purpose of flagella protein recognition. We propose to learn a multi-kernel classification function to approximate this optimal decision function, by minimizing the information loss of such approximation which is measured by the Kull back-Leibler (KL) divergence. An iterative multi-kernel classifier learning algorithm is developed to minimize the KL divergence for the problem of T. Brucei flagella protein recognition, experiments show its advantage over other T. Brucei flagellar protein recognition and multi-kernel learning methods. © 2014 IEEE.
    Citation
    Wang, J. J.-Y., & Gao, X. (2014). Minimum Information Loss Based Multi-kernel Learning for Flagellar Protein Recognition in Trypanosoma Brucei. 2014 IEEE International Conference on Data Mining Workshop. doi:10.1109/icdmw.2014.142
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2014 IEEE International Conference on Data Mining Workshop
    Conference/Event name
    14th IEEE International Conference on Data Mining Workshops, ICDMW 2014
    DOI
    10.1109/ICDMW.2014.142
    ae974a485f413a2113503eed53cd6c53
    10.1109/ICDMW.2014.142
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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