Novel feature selection method via kernel tensor decomposition for improved multi-omics data analysis.
In: BMC medical genomics, Jg. 15 (2022-02-24), Heft 1, S. 37
Online
academicJournal
Zugriff:
Background: Feature selection of multi-omics data analysis remains challenging owing to the size of omics datasets, comprising approximately [Formula: see text]-[Formula: see text] features. In particular, appropriate methods to weight individual omics datasets are unclear, and the approach adopted has substantial consequences for feature selection. In this study, we extended a recently proposed kernel tensor decomposition (KTD)-based unsupervised feature extraction (FE) method to integrate multi-omics datasets obtained from common samples in a weight-free manner.
Method: KTD-based unsupervised FE was reformatted as the collection of kernelized tensors sharing common samples, which was applied to synthetic and real datasets.
Results: The proposed advanced KTD-based unsupervised FE method showed comparative performance to that of the previously proposed KTD method, as well as tensor decomposition-based unsupervised FE, but required reduced memory and central processing unit time. Moreover, this advanced KTD method, specifically designed for multi-omics analysis, attributes P values to features, which is rare for existing multi-omics-oriented methods.
Conclusions: The sample R code is available at https://github.com/tagtag/MultiR/ .
(© 2022. The Author(s).)
| Titel: |
Novel feature selection method via kernel tensor decomposition for improved multi-omics data analysis.
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| Autor/in / Beteiligte Person: | Taguchi, YH ; Turki, T |
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| Zeitschrift: | BMC medical genomics, Jg. 15 (2022-02-24), Heft 1, S. 37 |
| Veröffentlichung: | London : BioMed Central, 2022 |
| Medientyp: | academicJournal |
| ISSN: | 1755-8794 (electronic) |
| DOI: | 10.1186/s12920-022-01181-4 |
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