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

Jim Jing-Yan Wang, Xin Gao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish (US)
Title of host publication2014 IEEE International Conference on Data Mining Workshop
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages133-141
Number of pages9
ISBN (Print)9781479942749
DOIs
StatePublished - Dec 2014

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