ArticleMetabolomics : Official journal of the Metabolomic Society2018
NS-kNN: a modified k-nearest neighbors approach for imputing metabolomics data.
Article in Metabolomics : Official journal of the Metabolomic Society, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it, 76 citations in OpenAlex.
- Disease classification for whole-blood DNA methylation: Meta-analysis, missing values imputation, and XAI.GigaScience · 2022Pooled it
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- Peripheral metabolomic profiling reveals lipid and amino acid alterations associated with immuno-inflammatory responses in treatment-naïve late-onset Alzheimer's disease.Frontiers in aging neuroscience · 2026Article
- Multiomics insight into disease trajectories of cardiometabolic diseases and cancer.Nature communications · 2025Article
- Artificial intelligence and systems biology analysis in stem cell research and therapeutics development.Stem cells translational medicine · 2025Review
- From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies.Molecular biotechnology · 2025Review
- Predicting the growth of asymptomatic small abdominal aortic aneurysms (AAA) based on deep learning.Frontiers in physiology · 2025Article
- Optimizing multi-omics data imputation with NMF and GAN synergy.Bioinformatics (Oxford, England) · 2024Article
- Assessing structural uncertainty of biochemical regulatory networks in metabolic pathways under varying data quality.NPJ systems biology and applications · 2024Article
- Patients with Neurodegenerative Proteinopathies Exhibit Altered Tryptophan Metabolism in the Serum and Cerebrospinal Fluid.ACS chemical neuroscience · 2024Article
- Discrimination of missing data types in metabolomics data based on particle swarm optimization algorithm and XGBoost model.Scientific reports · 2024Article
- Visual transformer and deep CNN prediction of high-risk COVID-19 infected patients using fusion of CT images and clinical data.BMC medical informatics and decision making · 2023Article
- Deep Learning Methods for Omics Data Imputation.Biology · 2023Review
- Alignment of multiple metabolomics LC-MS datasets from disparate diseases to reveal fever-associated metabolites.PLoS neglected tropical diseases · 2023Article
- Towards inferring absolute concentrations from relative abundance in time-course GC-MS metabolomics data.Molecular omics · 2023Article
- Article
- A machine learning-based data mining in medical examination data: a biological features-based biological age prediction model.BMC bioinformatics · 2022Article
- Mechanism-aware imputation: a two-step approach in handling missing values in metabolomics.BMC bioinformatics · 2022Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
introductionA common problem in metabolomics data analysis is the existence of a substantial number of missing values, which can complicate, bias, or even prevent certain downstream analyses. One of the most widely-used solutions to this problem is imputation of missing values using a k-nearest neighbors (kNN) algorithm to estimate missing metabolite abundances. kNN implicitly assumes that missing values are uniformly distributed at random in the dataset, but this is typically not true in metabolomics, where many values are missing because they are below the limit of detection of the analytical instrumentation.
objectivesHere, we explore the impact of nonuniformly distributed missing values (missing not at random, or MNAR) on imputation performance. We present a new model for generating synthetic missing data and a new algorithm, No-Skip kNN (NS-kNN), that accounts for MNAR values to provide more accurate imputations.
methodsWe compare the imputation errors of the original kNN algorithm using two distance metrics, NS-kNN, and a recently developed algorithm KNN-TN, when applied to multiple experimental datasets with different types and levels of missing data.
resultsOur results show that NS-kNN typically outperforms kNN when at least 20-30% of missing values in a dataset are MNAR. NS-kNN also has lower imputation errors than KNN-TN on realistic datasets when at least 50% of missing values are MNAR.
conclusionAccounting for the nonuniform distribution of missing values in metabolomics data can significantly improve the results of imputation algorithms. The NS-kNN method imputes missing metabolomics data more accurately than existing kNN-based approaches when used on realistic datasets.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.