Novel feature selection method via kernel tensor decomposition for improved multi-omics data analysis.

ABSTRACT:

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 unsup

SUBMITTER: Taguchi YH 

PROVIDER: S-EPMC8876179 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature