ArticleBioMed research international2017
Metabolomic Biomarker Identification in Presence of Outliers and Missing Values.
Article in BioMed research international, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Machine Learning and Artificial Intelligence in Nutrition Research: Analytical Methods, Applications, and Key Considerations.The Journal of nutrition · 2026Review
- Metabolomic Profiling Reveals Biomarkers in Coronary Heart Disease Comorbidity.Journal of diabetes research · 2024Article
- Medical prediction from missing data with max-minus negative regularized dropout.Frontiers in neuroscience · 2023Article
- How to deal with non-detectable and outlying values in biomarker research: Best practices and recommendations for univariate imputation approaches.Comprehensive psychoneuroendocrinology · 2021Review
- Kernel weighted least square approach for imputing missing values of metabolomics data.Scientific reports · 2021Article
- Targeted metabolomics analysis of postoperative delirium.Scientific reports · 2021Article
- An Efficient and Effective Model to Handle Missing Data in Classification.BioMed research international · 2020Article
- Metabolomic Profiling Revealed Potential Biomarkers in Patients With Moyamoya Disease.Frontiers in neuroscience · 2020Article
- Predictive Modeling for Metabolomics Data.Methods in molecular biology (Clifton, N.J.) · 2020Article
- Metabolomic profiling of matured coconut water during post-harvest storage revealed discrimination and distinct changes in metabolites.RSC advances · 2018Article
- Robust volcano plot: identification of differential metabolites in the presence of outliers.BMC bioinformatics · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metabolomics is the sophisticated and high-throughput technology based on the entire set of metabolites which is known as the connector between genotypes and phenotypes. For any phenotypic changes, potential metabolite (biomarker) identification is very important because it provides diagnostic as well as prognostic markers and can help to develop new biomolecular therapy. Biomarker identification from metabolomics data analysis is hampered by the use of high-throughput technology that provides high dimensional data matrix which contains missing values as well as outliers. However, missing value imputation and outliers handling techniques play important role in identifying biomarker correctly. Although several missing value imputation techniques are available, outliers deteriorate the accuracy of imputation as well as the accuracy of biomarker identification. Therefore, in this paper we have proposed a new biomarker identification technique combining the groupwise robust singular value decomposition,
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Registered trials
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