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- 한국산업응용수학회 JOURNAL OF THE KOREAN SOCIETY FOR INDUSTRIAL AND APPLIED MATHEMATICS Journal of the Korean Society for Industrial and Applied Mathematics Vol.28 No.4
- 발행연도
- 2024.12
- 수록면
- 226 - 242 (17page)
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Hypoxia, possibly leading to increased oxidative stress, is a consequence of metabolic alterations and can influence fetal growth as well as lifelong health. While various methods have been utilized to identify associations of metabolite concentration values with an outcome, hypoxia, studies assessing how the coexistence of ewe metabolites impacts the onset of hypoxia are at a nascent stage. Here, we present a novel approach to identify the associations of ewe metabolites and classify the outcome by including the interactions of ewe metabolites in the network. After using metabolite sub-networks as clusters, we implement lasso for logistic regression to determine the onset of hypoxia. We tested different clustering methods in order to validate the associations of ewe metabolites which may infer the mechanism of significant metabolic processes. We validated, in terms of accuracy and balanced F-score, the performance of classification method to determine whether the onset of hypoxemia is correctly identified. Our study shows that there exist strongly interacting ewe metabolites and that specific metabolites are strongly associated with hypoxia. The proposed approach can be applied to similarly structured metabolite datasets to predict health outcomes.
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