講演抄録/キーワード |
講演名 |
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 |
抄録 |
(和) |
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. |
(英) |
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. |
キーワード |
(和) |
principal component analysis / feature extraction / bioinformatics / FAMS / chooseLD / / / |
(英) |
principal component analysis / feature extraction / bioinformatics / FAMS / chooseLD / / / |
文献情報 |
信学技報, vol. 115, no. 112, IBISML2015-1, pp. 1-8, 2015年6月. |
資料番号 |
IBISML2015-1 |
発行日 |
2015-06-16 (IBISML) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
著作権に ついて |
技術研究報告に掲載された論文の著作権は電子情報通信学会に帰属します.(許諾番号:10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
PDFダウンロード |
IBISML2015-1 |
研究会情報 |
研究会 |
NC IPSJ-BIO IBISML IPSJ-MPS |
開催期間 |
2015-06-23 - 2015-06-25 |
開催地(和) |
沖縄科学技術大学院大学 |
開催地(英) |
Okinawa Institute of Science and Technology |
テーマ(和) |
機械学習によるバイオデータマインニング、一般 |
テーマ(英) |
Machine Learning Approach to Biodata Mining, and General |
講演論文情報の詳細 |
申込み研究会 |
IBISML |
会議コード |
2015-06-NC-BIO-IBISML-MPS |
本文の言語 |
英語 |
タイトル(和) |
|
サブタイトル(和) |
|
タイトル(英) |
Principal component analysis-based unsupervised feature extraction applied to in silico drug discovery for posttraumatic stress disorder-mediated heart disease |
サブタイトル(英) |
|
キーワード(1)(和/英) |
principal component analysis / principal component analysis |
キーワード(2)(和/英) |
feature extraction / feature extraction |
キーワード(3)(和/英) |
bioinformatics / bioinformatics |
キーワード(4)(和/英) |
FAMS / FAMS |
キーワード(5)(和/英) |
chooseLD / chooseLD |
キーワード(6)(和/英) |
/ |
キーワード(7)(和/英) |
/ |
キーワード(8)(和/英) |
/ |
第1著者 氏名(和/英/ヨミ) |
田口 善弘 / Y-h. Taguchi / タグチ ヨシヒロ |
第1著者 所属(和/英) |
中央大学 (略称: 中大)
Chuo University (略称: Chuo Univ) |
第2著者 氏名(和/英/ヨミ) |
岩舘 満雄 / Mitsuo Iwadate / イワダテ ミツオ |
第2著者 所属(和/英) |
中央大学 (略称: 中大)
Chuo University (略称: Chuo Univ) |
第3著者 氏名(和/英/ヨミ) |
梅山 秀明 / Hideaki Umeyama / ウメヤマ ヒデアキ |
第3著者 所属(和/英) |
中央大学 (略称: 中大)
Chuo University (略称: Chuo Univ) |
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講演者 |
第1著者 |
発表日時 |
2015-06-23 09:30:00 |
発表時間 |
25分 |
申込先研究会 |
IBISML |
資料番号 |
IBISML2015-1 |
巻番号(vol) |
vol.115 |
号番号(no) |
no.112 |
ページ範囲 |
pp.1-8 |
ページ数 |
8 |
発行日 |
2015-06-16 (IBISML) |
|