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 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) |
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IBISML2016-110 |
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) |
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topic model |
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gene expression |
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RNA-Seq |
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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 |
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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 |
6 |
Date of Issue |
2017-02-27 (IBISML) |
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