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Paper Abstract and Keywords
Presentation 2010-09-05 11:40
Computational identification of discriminating features of pathogenic and symbiotic Type III secreted effector proteins
Koji Yahara, Ying Jiang, Takashi Yanagawa (Kurume Univ.) PRMU2010-62 IBISML2010-34
Abstract (in Japanese) (See Japanese page) 
(in English) Type III secretion systems (T3SS) deliver bacterial proteins, or "effectors", into eukaryotic host cells, inducing physiological responses in the hosts. Effector proteins have been considered virulence factors of pathogenic bacteria, but T3SSs have now been found in symbiotic bacteria as well. Whether any physicochemical difference exists between the two types of effectors remains unknown. In this work, we combined computational statistical and machine-learning methods to identify features that could be responsible for the difference. For computational statistical method we used generalized Bayesian information criterion and kernel logistic regression, and for machine learning method we used support vector machine. It was clearly shown that differences in amino acid composition exist between pathogenic and symbiotic effector proteins. All identified discriminating features were those of amino acid composition and average residue weight, and their classification performance could be nearly identical to that using all physicochemical features, with sensitivity and specificity of over 80%. Further analysis on the seven discriminating features by graphical modeling revealed three dominant features among them. Moreover, amino acid regions that were distinctive for the seven features were explored by sliding window analysis. This study provides a methodological basis and important insights into the functional differences between pathogenic and symbiotic T3SS effectors.
Keyword (in Japanese) (See Japanese page) 
(in English) machine learning / nonparametric regression / feature selection / support vector machine / graphical modeling / bayesian network / bayesian information criterion /  
Reference Info. IEICE Tech. Rep., vol. 110, no. 188, IBISML2010-34, pp. 57-62, Sept. 2010.
Paper # IBISML2010-34 
Date of Issue 2010-08-29 (PRMU, IBISML) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF PRMU2010-62 IBISML2010-34

Conference Information
Committee IBISML PRMU IPSJ-CVIM  
Conference Date 2010-09-05 - 2010-09-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Fukuoka Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Machine learning and optimization for computer vision and pattern recognition, etc. 
Paper Information
Registration To IBISML 
Conference Code 2010-09-IBISML-PRMU 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Computational identification of discriminating features of pathogenic and symbiotic Type III secreted effector proteins 
Sub Title (in English)  
Keyword(1) machine learning  
Keyword(2) nonparametric regression  
Keyword(3) feature selection  
Keyword(4) support vector machine  
Keyword(5) graphical modeling  
Keyword(6) bayesian network  
Keyword(7) bayesian information criterion  
Keyword(8)  
1st Author's Name Koji Yahara  
1st Author's Affiliation Kurume University (Kurume Univ.)
2nd Author's Name Ying Jiang  
2nd Author's Affiliation Kurume University (Kurume Univ.)
3rd Author's Name Takashi Yanagawa  
3rd Author's Affiliation Kurume University (Kurume Univ.)
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Speaker Author-1 
Date Time 2010-09-05 11:40:00 
Presentation Time 30 minutes 
Registration for IBISML 
Paper # PRMU2010-62, IBISML2010-34 
Volume (vol) vol.110 
Number (no) no.187(PRMU), no.188(IBISML) 
Page pp.57-62 
#Pages 6 
Date of Issue 2010-08-29 (PRMU, IBISML) 

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