ArticleScientific reports2021
Identification of genes associated with altered gene expression and m6A profiles during hypoxia using tensor decomposition based unsupervised feature extraction.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Tensor Decomposition-Based Unsupervised Feature Extraction Applied to Bioinformatics.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Article
- Development of a Dual Drug-Loaded, Surfactant-Stabilized Contrast Agent Containing Oxygen.Polymers · 2022Article
- Novel feature selection method via kernel tensor decomposition for improved multi-omics data analysis.BMC medical genomics · 2022Article
- Classification and prediction for multi-cancer data with ultrahigh-dimensional gene expressions.PloS one · 2022Article
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2 authors.
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Abstract
Although hypoxia is a critical factor that can drive the progression of various diseases, the mechanism underlying hypoxia itself remains unclear. Recently, m6A has been proposed as an important factor driving hypoxia. Despite successful analyses, potential genes were not selected with statistical significance but were selected based solely on fold changes. Because the number of genes is large while the number of samples is small, it was impossible to select genes using conventional feature selection methods with statistical significance. In this study, we applied the recently proposed principal component analysis (PCA), tensor decomposition (TD), and kernel tensor decomposition (KTD)-based unsupervised feature extraction (FE) to a hypoxia data set. We found that PCA, TD, and KTD-based unsupervised FE could successfully identify a limited number of genes associated with altered gene expression and m6A profiles, as well as the enrichment of hypoxia-related biological terms, with improved statistical significance.
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