An evolutionary learning and network approach to identifying key metabolites for osteoarthritis
Metabolomics studies use quantitative analyses of metabolites from body fluids or tissues in order to investigate a sequence of cellular processes and biological systems in response to genetic and environmental influences. This promises an immense potential for a better understanding of the pathogen...
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ftrepec:oai:RePEc:plo:pcbi00:1005986 2024-04-14T08:15:10+00:00 An evolutionary learning and network approach to identifying key metabolites for osteoarthritis Ting Hu Karoliina Oksanen Weidong Zhang Ed Randell Andrew Furey Guang Sun Guangju Zhai https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005986 https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1005986&type=printable unknown https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005986 https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1005986&type=printable article ftrepec 2024-03-19T10:31:36Z Metabolomics studies use quantitative analyses of metabolites from body fluids or tissues in order to investigate a sequence of cellular processes and biological systems in response to genetic and environmental influences. This promises an immense potential for a better understanding of the pathogenesis of complex diseases. Most conventional metabolomics analysis methods exam one metabolite at a time and may overlook the synergistic effect of combining multiple metabolites. In this article, we proposed a new bioinformatics framework that infers the non-linear synergy among multiple metabolites using a symbolic model and subsequently, identify key metabolites using network analysis. Such a symbolic model is able to represent a complex non-linear relationship among a set of metabolites associated with osteoarthritis (OA) and is automatically learned using an evolutionary algorithm. Applied to the Newfoundland Osteoarthritis Study (NFOAS) dataset, our methodology was able to identify nine key metabolites including some known osteoarthritis-associated metabolites and some novel metabolic markers that have never been reported before. The results demonstrate the effectiveness of our methodology and more importantly, with further investigations, propose new hypotheses that can help better understand the OA disease.Author summary: Biomedical research has entered a new era where a large number of molecules and different components in biological systems can be quantitatively examined to investigate the causes of common human diseases. However, given the complexity of biological systems, those causes may not contribute to diseases individually but through interactions. The identification of those interactions, or the synergy of multiple factors, is a very challenging task due to the computational limitation, as well as the lack of effective methodologies for investigating multiple factors simultaneously. In this study, we proposed to model such an interaction effect through a self-learning algorithm using mechanisms ... Article in Journal/Newspaper Newfoundland RePEc (Research Papers in Economics) |
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Metabolomics studies use quantitative analyses of metabolites from body fluids or tissues in order to investigate a sequence of cellular processes and biological systems in response to genetic and environmental influences. This promises an immense potential for a better understanding of the pathogenesis of complex diseases. Most conventional metabolomics analysis methods exam one metabolite at a time and may overlook the synergistic effect of combining multiple metabolites. In this article, we proposed a new bioinformatics framework that infers the non-linear synergy among multiple metabolites using a symbolic model and subsequently, identify key metabolites using network analysis. Such a symbolic model is able to represent a complex non-linear relationship among a set of metabolites associated with osteoarthritis (OA) and is automatically learned using an evolutionary algorithm. Applied to the Newfoundland Osteoarthritis Study (NFOAS) dataset, our methodology was able to identify nine key metabolites including some known osteoarthritis-associated metabolites and some novel metabolic markers that have never been reported before. The results demonstrate the effectiveness of our methodology and more importantly, with further investigations, propose new hypotheses that can help better understand the OA disease.Author summary: Biomedical research has entered a new era where a large number of molecules and different components in biological systems can be quantitatively examined to investigate the causes of common human diseases. However, given the complexity of biological systems, those causes may not contribute to diseases individually but through interactions. The identification of those interactions, or the synergy of multiple factors, is a very challenging task due to the computational limitation, as well as the lack of effective methodologies for investigating multiple factors simultaneously. In this study, we proposed to model such an interaction effect through a self-learning algorithm using mechanisms ... |
format |
Article in Journal/Newspaper |
author |
Ting Hu Karoliina Oksanen Weidong Zhang Ed Randell Andrew Furey Guang Sun Guangju Zhai |
spellingShingle |
Ting Hu Karoliina Oksanen Weidong Zhang Ed Randell Andrew Furey Guang Sun Guangju Zhai An evolutionary learning and network approach to identifying key metabolites for osteoarthritis |
author_facet |
Ting Hu Karoliina Oksanen Weidong Zhang Ed Randell Andrew Furey Guang Sun Guangju Zhai |
author_sort |
Ting Hu |
title |
An evolutionary learning and network approach to identifying key metabolites for osteoarthritis |
title_short |
An evolutionary learning and network approach to identifying key metabolites for osteoarthritis |
title_full |
An evolutionary learning and network approach to identifying key metabolites for osteoarthritis |
title_fullStr |
An evolutionary learning and network approach to identifying key metabolites for osteoarthritis |
title_full_unstemmed |
An evolutionary learning and network approach to identifying key metabolites for osteoarthritis |
title_sort |
evolutionary learning and network approach to identifying key metabolites for osteoarthritis |
url |
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005986 https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1005986&type=printable |
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Newfoundland |
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Newfoundland |
op_relation |
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005986 https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1005986&type=printable |
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1796313434951778304 |