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Paper Abstract and Keywords
Presentation 2017-03-07 13:30
Topic model for analysis of gene expression data
Koji Iwayama (Ryukoku Univ.), Atsushi J. Nagano (Ryukoku Univ./Kyoto Univ.) IBISML2016-110
Abstract (in Japanese) (See Japanese page) 
(in English) The process, by which gene is used in the synthesis of a gene product through transcription and translation, is called expression. We can quantify gene expression by measuring the quantity of the gene transcription generated in this process. RNA-Seq allows a comprehensive gene expression using next-generation sequencing. Although higher sequencing depth can more accurately quantify gene expression, the number of samples is restricted by the higher cost. In this study, we proposed the new model for RNA-Seq data based on the topic model, which is mainly used in the field of natural language processing. In the proposed model, we use a negative binomial distribution rather than a multinomial distribution for generating words or genes. We applied the model to synthetic RNA-Seq data and demonstrated that the proposed model can precisely estimate true gene expression from RNA-Seq data with lower sequencing depth.
Keyword (in Japanese) (See Japanese page) 
(in English) topic model / gene expression / RNA-Seq / negative binomial distribution / / / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 500, IBISML2016-110, pp. 77-82, March 2017.
Paper # IBISML2016-110 
Date of Issue 2017-02-27 (IBISML) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
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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)
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Conference Information
Committee IBISML  
Conference Date 2017-03-06 - 2017-03-07 
Place (in Japanese) (See Japanese page) 
Place (in English) Tokyo Institute of Technology 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Statistical Mathematics, Machine Learning, Data Mining, etc. 
Paper Information
Registration To IBISML 
Conference Code 2017-03-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Topic model for analysis of gene expression data 
Sub Title (in English)  
Keyword(1) topic model  
Keyword(2) gene expression  
Keyword(3) RNA-Seq  
Keyword(4) negative binomial distribution  
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1st Author's Name Koji Iwayama  
1st Author's Affiliation Ryukoku University (Ryukoku Univ.)
2nd Author's Name Atsushi J. Nagano  
2nd Author's Affiliation Ryukoku University/Kyoto University (Ryukoku Univ./Kyoto Univ.)
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Speaker Author-1 
Date Time 2017-03-07 13:30:00 
Presentation Time 30 minutes 
Registration for IBISML 
Paper # IBISML2016-110 
Volume (vol) vol.116 
Number (no) no.500 
Page pp.77-82 
#Pages
Date of Issue 2017-02-27 (IBISML) 


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