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) |
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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) |
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Keyword(1) |
principal component analysis |
Keyword(2) |
feature extraction |
Keyword(3) |
bioinformatics |
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FAMS |
Keyword(5) |
chooseLD |
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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 |
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Chuo University (Chuo Univ) |
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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 |
8 |
Date of Issue |
2015-06-16 (IBISML) |
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