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

Handle URI:
http://hdl.handle.net/10754/565847
Title:
Minimum Information Loss Based Multi-kernel Learning for Flagellar Protein Recognition in Trypanosoma Brucei
Authors:
Wang, Jim Jing-Yan; Gao, Xin ( 0000-0002-7108-3574 )
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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program; Computational Bioscience Research Center (CBRC)
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
Issue Date:
Dec-2014
DOI:
10.1109/ICDMW.2014.142
Type:
Conference Paper
Appears in Collections:
Conference Papers; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorWang, Jim Jing-Yanen
dc.contributor.authorGao, Xinen
dc.date.accessioned2015-08-11T13:43:10Zen
dc.date.available2015-08-11T13:43:10Zen
dc.date.issued2014-12en
dc.identifier.doi10.1109/ICDMW.2014.142en
dc.identifier.urihttp://hdl.handle.net/10754/565847en
dc.description.abstractTrypanosma 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.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.subjectFlagellar proteinen
dc.subjectInformation Lossen
dc.subjectKullback Leibler divergenceen
dc.subjectMulti-Kernel Learningen
dc.subjectTrypanosma bruceien
dc.titleMinimum Information Loss Based Multi-kernel Learning for Flagellar Protein Recognition in Trypanosoma Bruceien
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.identifier.journal2014 IEEE International Conference on Data Mining Workshopen
dc.conference.date14 December 2014en
dc.conference.name14th IEEE International Conference on Data Mining Workshops, ICDMW 2014en
kaust.authorWang, Jim Jing-Yanen
kaust.authorGao, Xinen
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