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KODAMA exploratory analysis in metabolic phenotyping
KODAMA is a valuable tool in metabolomics research to perform exploratory analysis. The advanced analytical technologies commonly used for metabolic phenotyping, mass spectrometry, and nuclear magnetic resonance spectroscopy push out a bunch of high-dimensional data. These complex datasets necessita...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9887019/ https://www.ncbi.nlm.nih.gov/pubmed/36733493 http://dx.doi.org/10.3389/fmolb.2022.1070394 |
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author | Zinga, Maria Mgella Abdel-Shafy, Ebtesam Melak, Tadele Vignoli, Alessia Piazza, Silvano Zerbini, Luiz Fernando Tenori, Leonardo Cacciatore, Stefano |
author_facet | Zinga, Maria Mgella Abdel-Shafy, Ebtesam Melak, Tadele Vignoli, Alessia Piazza, Silvano Zerbini, Luiz Fernando Tenori, Leonardo Cacciatore, Stefano |
author_sort | Zinga, Maria Mgella |
collection | PubMed |
description | KODAMA is a valuable tool in metabolomics research to perform exploratory analysis. The advanced analytical technologies commonly used for metabolic phenotyping, mass spectrometry, and nuclear magnetic resonance spectroscopy push out a bunch of high-dimensional data. These complex datasets necessitate tailored statistical analysis able to highlight potentially interesting patterns from a noisy background. Hence, the visualization of metabolomics data for exploratory analysis revolves around dimensionality reduction. KODAMA excels at revealing local structures in high-dimensional data, such as metabolomics data. KODAMA has a high capacity to detect different underlying relationships in experimental datasets and correlate extracted features with accompanying metadata. Here, we describe the main application of KODAMA exploratory analysis in metabolomics research. |
format | Online Article Text |
id | pubmed-9887019 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98870192023-02-01 KODAMA exploratory analysis in metabolic phenotyping Zinga, Maria Mgella Abdel-Shafy, Ebtesam Melak, Tadele Vignoli, Alessia Piazza, Silvano Zerbini, Luiz Fernando Tenori, Leonardo Cacciatore, Stefano Front Mol Biosci Molecular Biosciences KODAMA is a valuable tool in metabolomics research to perform exploratory analysis. The advanced analytical technologies commonly used for metabolic phenotyping, mass spectrometry, and nuclear magnetic resonance spectroscopy push out a bunch of high-dimensional data. These complex datasets necessitate tailored statistical analysis able to highlight potentially interesting patterns from a noisy background. Hence, the visualization of metabolomics data for exploratory analysis revolves around dimensionality reduction. KODAMA excels at revealing local structures in high-dimensional data, such as metabolomics data. KODAMA has a high capacity to detect different underlying relationships in experimental datasets and correlate extracted features with accompanying metadata. Here, we describe the main application of KODAMA exploratory analysis in metabolomics research. Frontiers Media S.A. 2023-01-17 /pmc/articles/PMC9887019/ /pubmed/36733493 http://dx.doi.org/10.3389/fmolb.2022.1070394 Text en Copyright © 2023 Zinga, Abdel-Shafy, Melak, Vignoli, Piazza, Zerbini, Tenori and Cacciatore. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Molecular Biosciences Zinga, Maria Mgella Abdel-Shafy, Ebtesam Melak, Tadele Vignoli, Alessia Piazza, Silvano Zerbini, Luiz Fernando Tenori, Leonardo Cacciatore, Stefano KODAMA exploratory analysis in metabolic phenotyping |
title | KODAMA exploratory analysis in metabolic phenotyping |
title_full | KODAMA exploratory analysis in metabolic phenotyping |
title_fullStr | KODAMA exploratory analysis in metabolic phenotyping |
title_full_unstemmed | KODAMA exploratory analysis in metabolic phenotyping |
title_short | KODAMA exploratory analysis in metabolic phenotyping |
title_sort | kodama exploratory analysis in metabolic phenotyping |
topic | Molecular Biosciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9887019/ https://www.ncbi.nlm.nih.gov/pubmed/36733493 http://dx.doi.org/10.3389/fmolb.2022.1070394 |
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