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Novelty detection for metabolic dynamics established on breast cancer tissue using 2D NMR TOCSY spectra

Most metabolic profiling approaches focus only on identifying pre-known metabolites on NMR TOCSY spectrum using configured parameters. However, there is a lack of tasks dealing with automating the detection of new metabolites that might appear during the dynamic evolution of biological cells. Novelt...

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Autores principales: Migdadi, Lubaba, Telfah, Ahmad, Hergenröder, Roland, Wöhler, Christian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9213235/
https://www.ncbi.nlm.nih.gov/pubmed/35782733
http://dx.doi.org/10.1016/j.csbj.2022.05.050
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author Migdadi, Lubaba
Telfah, Ahmad
Hergenröder, Roland
Wöhler, Christian
author_facet Migdadi, Lubaba
Telfah, Ahmad
Hergenröder, Roland
Wöhler, Christian
author_sort Migdadi, Lubaba
collection PubMed
description Most metabolic profiling approaches focus only on identifying pre-known metabolites on NMR TOCSY spectrum using configured parameters. However, there is a lack of tasks dealing with automating the detection of new metabolites that might appear during the dynamic evolution of biological cells. Novelty detection is a category of machine learning that is used to identify data that emerge during the test phase and were not considered during the training phase. We propose a novelty detection system for detecting novel metabolites in the 2D NMR TOCSY spectrum of a breast cancer-tissue sample. We build one- and multi-class recognition systems using different classifiers such as, Kernel Null Foley-Sammon Transform, Kernel Density Estimation, and Support Vector Data Description. The training models were constructed based on different sizes of training data and are used in the novelty detection procedure. Multiple evaluation measures were applied to test the performance of the novelty detection methods. Depending on the training data size, all classifiers were able to achieve 0% false positive rates and total misclassification error in addition to 100% true positive rates. The median total time for the novelty detection process varies between 1.5 and 20 seconds, depending on the classifier and the amount of training data. The results of our novel metabolic profiling method demonstrate its suitability, robustness and speed in automated metabolic research.
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spelling pubmed-92132352022-07-01 Novelty detection for metabolic dynamics established on breast cancer tissue using 2D NMR TOCSY spectra Migdadi, Lubaba Telfah, Ahmad Hergenröder, Roland Wöhler, Christian Comput Struct Biotechnol J Research Article Most metabolic profiling approaches focus only on identifying pre-known metabolites on NMR TOCSY spectrum using configured parameters. However, there is a lack of tasks dealing with automating the detection of new metabolites that might appear during the dynamic evolution of biological cells. Novelty detection is a category of machine learning that is used to identify data that emerge during the test phase and were not considered during the training phase. We propose a novelty detection system for detecting novel metabolites in the 2D NMR TOCSY spectrum of a breast cancer-tissue sample. We build one- and multi-class recognition systems using different classifiers such as, Kernel Null Foley-Sammon Transform, Kernel Density Estimation, and Support Vector Data Description. The training models were constructed based on different sizes of training data and are used in the novelty detection procedure. Multiple evaluation measures were applied to test the performance of the novelty detection methods. Depending on the training data size, all classifiers were able to achieve 0% false positive rates and total misclassification error in addition to 100% true positive rates. The median total time for the novelty detection process varies between 1.5 and 20 seconds, depending on the classifier and the amount of training data. The results of our novel metabolic profiling method demonstrate its suitability, robustness and speed in automated metabolic research. Research Network of Computational and Structural Biotechnology 2022-06-01 /pmc/articles/PMC9213235/ /pubmed/35782733 http://dx.doi.org/10.1016/j.csbj.2022.05.050 Text en © 2022 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Migdadi, Lubaba
Telfah, Ahmad
Hergenröder, Roland
Wöhler, Christian
Novelty detection for metabolic dynamics established on breast cancer tissue using 2D NMR TOCSY spectra
title Novelty detection for metabolic dynamics established on breast cancer tissue using 2D NMR TOCSY spectra
title_full Novelty detection for metabolic dynamics established on breast cancer tissue using 2D NMR TOCSY spectra
title_fullStr Novelty detection for metabolic dynamics established on breast cancer tissue using 2D NMR TOCSY spectra
title_full_unstemmed Novelty detection for metabolic dynamics established on breast cancer tissue using 2D NMR TOCSY spectra
title_short Novelty detection for metabolic dynamics established on breast cancer tissue using 2D NMR TOCSY spectra
title_sort novelty detection for metabolic dynamics established on breast cancer tissue using 2d nmr tocsy spectra
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9213235/
https://www.ncbi.nlm.nih.gov/pubmed/35782733
http://dx.doi.org/10.1016/j.csbj.2022.05.050
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