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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2022
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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. |
format | Online Article Text |
id | pubmed-9213235 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT migdadilubaba noveltydetectionformetabolicdynamicsestablishedonbreastcancertissueusing2dnmrtocsyspectra AT telfahahmad noveltydetectionformetabolicdynamicsestablishedonbreastcancertissueusing2dnmrtocsyspectra AT hergenroderroland noveltydetectionformetabolicdynamicsestablishedonbreastcancertissueusing2dnmrtocsyspectra AT wohlerchristian noveltydetectionformetabolicdynamicsestablishedonbreastcancertissueusing2dnmrtocsyspectra |