Minimum Information Loss Based Multi-kernel Learning for Flagellar Protein Recognition in Trypanosoma Brucei
Type
Conference PaperAuthors
Wang, Jim Jing-YanGao, Xin

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionComputer Science Program
Computational Bioscience Research Center (CBRC)
Date
2014-12Permanent link to this record
http://hdl.handle.net/10754/565847
Metadata
Show full item recordAbstract
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.2014.142Conference/Event name
14th IEEE International Conference on Data Mining Workshops, ICDMW 2014ae974a485f413a2113503eed53cd6c53
10.1109/ICDMW.2014.142