Tensor-Decomposition-Based Unsupervised Feature Extraction Applied to Prostate Cancer Multiomics Data

Tensor-Decomposition-Based Unsupervised Feature Extraction Applied to Prostate Cancer Multiomics Data

The large <i>p</i> small <i>n</i> problem is a challenge without a de facto standard method available to it. In this study, we propose a tensor-decomposition (TD)-based unsupervised feature extraction (FE) formalism applied to multiomics datasets, in which the number of featu...

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Bibliographic Details
Main Authors: Y-h. Taguchi, Turki Turki
Format: Article
Language:English
Published: MDPI AG 2020-12-01
Series:Genes
Subjects:
prostate cancer
gene expression
genomic regions
protien-coding genes
tensor decomposition
unsupervised learning
Online Access:https://www.mdpi.com/2073-4425/11/12/1493

Internet

https://www.mdpi.com/2073-4425/11/12/1493

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