AUTHOR=Taguchi Y-h. , Turki Turki 

TITLE=Application note: TDbasedUFE and TDbasedUFEadv: bioconductor packages to perform tensor decomposition based unsupervised feature extraction

JOURNAL=Frontiers in Artificial Intelligence

VOLUME=6

YEAR=2023

URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2023.1237542

DOI=10.3389/frai.2023.1237542

ISSN=2624-8212

ABSTRACT=<sec><title>Motivation</title><p>Tensor decomposition (TD)-based unsupervised feature extraction (FE) has proven effective for a wide range of bioinformatics applications ranging from biomarker identification to the identification of disease-causing genes and drug repositioning. However, TD-based unsupervised FE failed to gain widespread acceptance due to the lack of user-friendly tools for non-experts.</p></sec><sec><title>Results</title><p>We developed two bioconductor packages—TDbasedUFE and TDbasedUFEadv—that enable researchers unfamiliar with TD to utilize TD-based unsupervised FE. The packages facilitate the identification of differentially expressed genes and multiomics analysis. TDbasedUFE was found to outperform two state-of-the-art methods, such as DESeq2 and DIABLO.</p></sec><sec><title>Availability and implementation</title><p>TDbasedUFE and TDbasedUFEadv are freely available as R/Bioconductor packages, which can be accessed at <ext-link ext-link-type="uri" xlink:href="https://bioconductor.org/packages/TDbasedUFE" xmlns:xlink="http://www.w3.org/1999/xlink">https://bioconductor.org/packages/TDbasedUFE</ext-link> and <ext-link ext-link-type="uri" xlink:href="https://bioconductor.org/packages/TDbasedUFEadv" xmlns:xlink="http://www.w3.org/1999/xlink">https://bioconductor.org/packages/TDbasedUFEadv</ext-link>, respectively.</p></sec>