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Sparse kernel canonical correlation analysis for discovery of nonlinear interactions in high-dimensional data
https://oist.repo.nii.ac.jp/records/226
https://oist.repo.nii.ac.jp/records/2268ae9dbab-b4f0-4959-9cea-f59c589826cb
名前 / ファイル | ライセンス | アクション |
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Yoshida et al. BMC Bioinformatics (2017) 18_108 (1.4 MB)
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Creative Commons Attribution 4.0 International
(http://creativecommons.org/licenses/by/4.0/) |
Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2018-01-18 | |||||
タイトル | ||||||
言語 | en | |||||
タイトル | Sparse kernel canonical correlation analysis for discovery of nonlinear interactions in high-dimensional data | |||||
言語 | ||||||
言語 | eng | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
資源タイプ | journal article | |||||
著者(英) |
Yoshida, Kosuke
× Yoshida, Kosuke× Yoshimoto, Junichiro× Doya, Kenji |
|||||
書誌情報 |
en : BMC Bioinformatics 巻 18, p. 108, 発行日 2017-02-14 |
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抄録 | ||||||
内容記述タイプ | Other | |||||
内容記述 | Advance in high-throughput technologies in genomics, transcriptomics, and metabolomics has created demand for bioinformatics tools to integrate high-dimensional data from different sources. Canonical correlation analysis (CCA) is a statistical tool for finding linear associations between different types of information. Previous extensions of CCA used to capture nonlinear associations, such as kernel CCA, did not allow feature selection or capturing of multiple canonical components. Here we propose a novel method, two-stage kernel CCA (TSKCCA) to select appropriate kernels in the framework of multiple kernel learning.TSKCCA first selects relevant kernels based on the HSIC criterion in the multiple kernel learning framework. Weights are then derived by non-negative matrix decomposition with L1 regularization. Using artificial datasets and nutrigenomic datasets, we show that TSKCCA can extract multiple, nonlinear associations among high-dimensional data and multiplicative interactions among variables.TSKCCA can identify nonlinear associations among high-dimensional data more reliably than previous nonlinear CCA methods. | |||||
出版者 | ||||||
出版者 | BMC | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 1471-2105 | |||||
PubMed番号 | ||||||
関連タイプ | isIdenticalTo | |||||
識別子タイプ | PMID | |||||
関連識別子 | info:pmid/28196464 | |||||
DOI | ||||||
関連タイプ | isIdenticalTo | |||||
識別子タイプ | DOI | |||||
関連識別子 | info:doi/10.1186/s12859-017-1543-x | |||||
権利 | ||||||
権利情報 | © 2017 The Author(s) | |||||
関連サイト | ||||||
識別子タイプ | URI | |||||
関連識別子 | https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1543-x | |||||
著者版フラグ | ||||||
出版タイプ | VoR | |||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 |