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| Presentation | 2014-11-17 17:00 [Poster Presentation] Heuristic principal component analysis based unsupervised feature extraction and its application to bioinformatics Y-h. Taguchi (Chuo Univ)  IBISML2014-46 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | : Feature extraction (FE) is a difficult task when the number of features is much larger than the number of samples, although that is a typical situation when biological (big) data is analyzed. This is especially true when FE is stable, independent of the samples considered (stable FE), and is often required. However, the stability of FE has not been considered seriously. In this poster, we demonstrate that principal component analysis (PCA) based unsupervised FE functions as stable FE. Three bioinformatics applications of PCA based unsupervised FE: 1. detection of aberrant DNA methylation associated with diseases [1- 2], 2. biomarker identification using circulating microRNA [3-4] and 3. proteomic analysis of bacterial culturing processes [5], are discussed. In the first application, we have treated two examples: identification of genes with aberrant promoter methylation commonly associated with three autoimmune diseases [1] and identification of genes with genotype specific aberrant DNA methylation associated with Esophageal squamous cell carcinoma[2]. For both applications, we have successfully identified genes with significant probabilities. In the second application, we have also treated two examples: identification of blood miRNAs discriminating between three liver inflammatory diseases and health controls [3] and identification of blood miRNAs discriminating 14 diseases and healthy controls. For these two applications. We have successfully identified sets of limited number (about ten) of miRNAs discriminating samples by 0.8 to 0.9 accuracies. In the third application, we have applied PCA based unsupervised FE to culturing processes of bacteria, S. pyogenes that often causes life-threading diseases. In this application, our method successfully identified critical proteins in culturing processes of bacteria, In conclusion, PCA based unsupervised FE is promising method which can be applied to wide range of bioinformatics applications. References: 1) S. Ishida et al, (2014) Bioinformatic screening of autoimmune disease genes and protein structure prediction with FAMS for drug discovery, Protein Pept Lett. in press.(PMID: 23855671) 2) R. Kinoshita et al, (2014) Genes associated with genotype-specific DNA methylation in squamous cell carcinoma as candidate drug targets, BMC Syst Biol. 8(S1):S4. 3) Y-h. Taguchi and Y. Murakami, (2013) Principal Component Analysis Based Feature Extraction Approach to Identify Circulating microRNA Biomarkers, PLoS ONE, 8(6):e66714. 4) Y. Murakami et al, (2012) Comprehensive miRNA expression analysis in peripheral blood can diagnose liver disease PLoS ONE, 7(10):e48366. 5) YH Taguchi, Akira Okamoto (2012) Principal Component Analysis for Bacterial Proteomic Analysis, Pattern Recognition in Bioinformatics 2012, Lecture Notes in Computer Science, Vol. 7632, PP.141-152 |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | principal component analysis / feature extraction / bioinformatics / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 114, no. 306, IBISML2014-46, pp. 87-94, Nov. 2014. | |
| Paper # | IBISML2014-46 | |
| Date of Issue | 2014-11-10 (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 | IBISML2014-46 |
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| Conference Information | |
| Committee | IBISML |
| Conference Date | 2014-11-17 - 2014-11-19 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Nagoya Univ. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | IBISML |
| Conference Code | 2014-11-IBISML |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Heuristic principal component analysis based unsupervised feature extraction and its application to bioinformatics |
| Sub Title (in English) | |
| Keyword(1) | principal component analysis |
| Keyword(2) | feature extraction |
| Keyword(3) | bioinformatics |
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| 1st Author's Name | Y-h. Taguchi |
| 1st Author's Affiliation | Chui University (Chuo Univ) |
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| Speaker | Author-1 |
| Date Time | 2014-11-17 17:00:00 |
| Presentation Time | 180 minutes |
| Registration for | IBISML |
| Paper # | IBISML2014-46 |
| Volume (vol) | vol.114 |
| Number (no) | no.306 |
| Page | pp.87-94 |
| #Pages | 8 |
| Date of Issue | 2014-11-10 (IBISML) |
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