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    Please use this identifier to cite or link to this item: https://ir.csmu.edu.tw:8080/ir/handle/310902500/23845


    Title: wiSDOM: a visual and statistical analytics for interrogating microbiome
    Authors: Su, SC;Galvin, JE;Yang, SF;Chung, WH;Chang, LC
    Date: 2021
    Issue Date: 2022-08-05T09:43:47Z (UTC)
    Publisher: OXFORD UNIV PRESS
    ISSN: 1367-4803
    Abstract: Motivation: We proposed a wiSDOM (web-based inclusionary analysis Suite for Disease-Oriented Metagenomics) R Shiny application which comprises six functional modules: (i) initial visualization of sampling effort and distribution of dominant bacterial taxa among groups or individual samples at different taxonomic levels; (ii) statistical and visual analysis of alpha diversity; (iii) analysis of similarity (ANOSIM) of beta diversity on UniFrac, Bray-Curtis, Horn-Morisita or Jaccard distance and visualizations; (iv) microbial biomarker discovery between two or more groups with various statistical and machine learning approaches; (v) assessment of the clinical validity of selected biomarkers by creating the interactive receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC) for binary classifiers; and lastly (vi) functional prediction of metagenomes with PICRUSt or Tax4Fun. Results: The performance of wiSDOM has been evaluated in several of our previous studies for exploring microbial biomarkers and their clinical validity as well as assessing the alterations in bacterial diversity and functionality. The wiSDOM can be customized and visualized as per users' needs and specifications, allowing researchers without programming background to conduct comprehensive data mining and illustration using an intuitive browser-based interface.
    URI: http://dx.doi.org/10.1093/bioinformatics/btab057
    https://www.webofscience.com/wos/woscc/full-record/WOS:000697377500050
    https://ir.csmu.edu.tw:8080/handle/310902500/23845
    Relation: BIOINFORMATICS ,2021,v37,issue 17, P2795-2797
    Appears in Collections:[中山醫學大學研究成果] 期刊論文

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