ArticleScientific reports2018
Application of kernel principal component analysis and computational machine learning to exploration of metabolites strongly associated with diet.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Integrated Metabolomics-KPCA-Machine Learning framework: a solution for geographical traceability of Chinese Jujube.Food chemistry: X · 2025Article
- Improving genomic prediction accuracy for methane emission and feed efficiency in sheep: integrating rumen microbial PCA with host genomic variation using neural network GBLUP (NN-GBLUP).Genetics, selection, evolution : GSE · 2025Article
- Leveraging ML for profiling lipidomic alterations in breast cancer tissues: a methodological perspective.Scientific reports · 2024Article
- Exploring metabolic anomalies in COVID-19 and post-COVID-19: a machine learning approach with explainable artificial intelligence.Frontiers in molecular biosciences · 2024Article
- Primary Metabolite Screening Shows Significant Differences between Embryogenic and Non-Embryogenic Callus of Tamarillo (Plants (Basel, Switzerland) · 2023Article
- An agroecological structure model of compost-soil-plant interactions for sustainable organic farming.ISME communications · 2023Article
- Screening for obstructive sleep apnea in patients with cancer - a machine learning approach.Sleep advances : a journal of the Sleep Research Society · 2023Article
- Integrative measurement analysis via machine learning descriptor selection for investigating physical properties of biopolymers in hairs.Scientific reports · 2021Article
- Nutritional Metabolomics and the Classification of Dietary Biomarker Candidates: A Critical Review.Advances in nutrition (Bethesda, Md.) · 2021Review
- The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science.RSC advances · 2021Review
- Relaxometric learning: a pattern recognition method for TBMC chemistry · 2021Article
- InterSpin: Integrated Supportive Webtools for Low- and High-Field NMR Analyses Toward Molecular Complexity.ACS omega · 2019Article
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Authors and funding
3 authors.
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Abstract
Computer-based technological innovation provides advancements in sophisticated and diverse analytical instruments, enabling massive amounts of data collection with relative ease. This is accompanied by a fast-growing demand for technological progress in data mining methods for analysis of big data derived from chemical and biological systems. From this perspective, use of a general "linear" multivariate analysis alone limits interpretations due to "non-linear" variations in metabolic data from living organisms. Here we describe a kernel principal component analysis (KPCA)-incorporated analytical approach for extracting useful information from metabolic profiling data. To overcome the limitation of important variable (metabolite) determinations, we incorporated a random forest conditional variable importance measure into our KPCA-based analytical approach to demonstrate the relative importance of metabolites. Using a market basket analysis, hippurate, the most important variable detected in the importance measure, was associated with high levels of some vitamins and minerals present in foods eaten the previous day, suggesting a relationship between increased hippurate and intake of a wide variety of vegetables and fruits. Therefore, the KPCA-incorporated analytical approach described herein enabled us to capture input-output responses, and should be useful not only for metabolic profiling but also for profiling in other areas of biological and environmental systems.
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