IEICE Technical Committee Submission System
Conference Paper's Information
Online Proceedings
[Sign in]
Tech. Rep. Archives
 Go Top Page Go Previous   [Japanese] / [English] 

Paper Abstract and Keywords
Presentation 2015-06-23 09:30
Principal component analysis-based unsupervised feature extraction applied to in silico drug discovery for posttraumatic stress disorder-mediated heart disease
Y-h. Taguchi, Mitsuo Iwadate, Hideaki Umeyama (Chuo Univ) IBISML2015-1
Abstract (in Japanese) (See Japanese page) 
(in English) Background Feature extraction (FE) is difficult, particularly if there are more features than samples, as small
sample numbers often result in biased outcomes or overfitting. Furthermore, multiple sample classes often complicate
FE because evaluating performance, which is usual in supervised FE, is generally harder than the two-class problem.
Developing sample classification independent unsupervised methods would solve many of these problems.
Results Two principal component analysis (PCA)-based FE, specifically, variational Bayes PCA (VBPCA) was
extended to perform unsupervised FE, and together with conventional PCA (CPCA)-based unsupervised FE, were
tested as sample classification independent unsupervised FE methods. VBPCA- and CPCA-based unsupervised FE
both performed well when applied to simulated data, and a posttraumatic stress disorder (PTSD)-mediated heart
disease data set that had multiple categorical class observations in mRNA/microRNA expression of stressed mouse
heart. A critical set of PTSD miRNAs/mRNAs were identified that show aberrant expression between treatment
and control samples, and significant, negative correlation with one another. Moreover, greater stability and biological
feasibility than conventional supervised FE was also demonstrated. Based on the results obtained, in silico drug
discovery was performed as translational validation of the methods.
Conclusions Our two proposed unsupervised FE methods (CPCA- and VBPCA-based) worked well on simulated
data, and outperformed two conventional supervised FE methods on a real data set. Thus, these two methods have
suggested equivalence for FE on categorical multiclass data sets, with potential translational utility for in silico drug
discovery.
Keyword (in Japanese) (See Japanese page) 
(in English) principal component analysis / feature extraction / bioinformatics / FAMS / chooseLD / / /  
Reference Info. IEICE Tech. Rep., vol. 115, no. 112, IBISML2015-1, pp. 1-8, June 2015.
Paper # IBISML2015-1 
Date of Issue 2015-06-16 (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 IBISML2015-1

Conference Information
Committee NC IPSJ-BIO IBISML IPSJ-MPS  
Conference Date 2015-06-23 - 2015-06-25 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Institute of Science and Technology 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Machine Learning Approach to Biodata Mining, and General 
Paper Information
Registration To IBISML 
Conference Code 2015-06-NC-BIO-IBISML-MPS 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Principal component analysis-based unsupervised feature extraction applied to in silico drug discovery for posttraumatic stress disorder-mediated heart disease 
Sub Title (in English)  
Keyword(1) principal component analysis  
Keyword(2) feature extraction  
Keyword(3) bioinformatics  
Keyword(4) FAMS  
Keyword(5) chooseLD  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Y-h. Taguchi  
1st Author's Affiliation Chuo University (Chuo Univ)
2nd Author's Name Mitsuo Iwadate  
2nd Author's Affiliation Chuo University (Chuo Univ)
3rd Author's Name Hideaki Umeyama  
3rd Author's Affiliation Chuo University (Chuo Univ)
4th Author's Name  
4th Author's Affiliation ()
5th Author's Name  
5th Author's Affiliation ()
6th Author's Name  
6th Author's Affiliation ()
7th Author's Name  
7th Author's Affiliation ()
8th Author's Name  
8th Author's Affiliation ()
9th Author's Name  
9th Author's Affiliation ()
10th Author's Name  
10th Author's Affiliation ()
11th Author's Name  
11th Author's Affiliation ()
12th Author's Name  
12th Author's Affiliation ()
13th Author's Name  
13th Author's Affiliation ()
14th Author's Name  
14th Author's Affiliation ()
15th Author's Name  
15th Author's Affiliation ()
16th Author's Name  
16th Author's Affiliation ()
17th Author's Name  
17th Author's Affiliation ()
18th Author's Name  
18th Author's Affiliation ()
19th Author's Name  
19th Author's Affiliation ()
20th Author's Name  
20th Author's Affiliation ()
Speaker Author-1 
Date Time 2015-06-23 09:30:00 
Presentation Time 25 minutes 
Registration for IBISML 
Paper # IBISML2015-1 
Volume (vol) vol.115 
Number (no) no.112 
Page pp.1-8 
#Pages
Date of Issue 2015-06-16 (IBISML) 


[Return to Top Page]

[Return to IEICE Web Page]


The Institute of Electronics, Information and Communication Engineers (IEICE), Japan