Random forest-based imputation outperforms other methods for imputing LC-MS metabolomics data: a comparative study

GSimp: A Gibbs sampler based left-censored missing value imputation approach for metabolomics studies

(2018) Runmin Wei et al.   PLoS Computational Biology

Random forest missing data algorithms

(2017) Fei Tang et al.   Statistical Analysis and Data Mining

Indolepropionic acid and novel lipid metabolites are associated with a lower risk of type 2 diabetes in the Finnish Diabetes Prevention Study

(2017) Vanessa D. de Mello et al.   Scientific Reports

Nearest neighbor imputation algorithms: a critical evaluation

(2016) Lorenzo Beretta et al.   BMC Medical Informatics and Decision Making

Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies

(2016) Cosmin Lazar et al.   JOURNAL OF PROTEOME RESEARCH

Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling

(2016) Riccardo Di Guida et al.   Metabolomics

Egg consumption and risk of incident type 2 diabetes in men: the Kuopio Ischaemic Heart Disease Risk Factor Study

(2015) Jyrki K Virtanen et al.   AMERICAN JOURNAL OF CLINICAL NUTRITION

Missing value imputation strategies for metabolomics data

(2015) Emily Grace Armitage et al.   ELECTROPHORESIS

Review, Evaluation, and Discussion of the Challenges of Missing Value Imputation for Mass Spectrometry-Based Label-Free Global Proteomics

(2015) Bobbie-Jo M. Webb-Robertson et al.   JOURNAL OF PROTEOME RESEARCH

Effects of ellagitannin-rich berries on blood lipids, gut microbiota, and urolithin production in human subjects with symptoms of metabolic syndrome

(2013) Riitta Puupponen-Pimiä et al.   MOLECULAR NUTRITION & FOOD RESEARCH

Bilberries reduce low-grade inflammation in individuals with features of metabolic syndrome

(2012) Marjukka Kolehmainen et al.   MOLECULAR NUTRITION & FOOD RESEARCH

Probabilistic principal component analysis for metabolomic data

(2011) Gift Nyamundanda et al.   BMC BIOINFORMATICS

On the choice of the best imputation methods for missing values considering three groups of classification methods

(2011) Julián Luengo et al.   KNOWLEDGE AND INFORMATION SYSTEMS

Missing values in mass spectrometry based metabolomics: an undervalued step in the data processing pipeline

(2011) Olga Hrydziuszko et al.   Metabolomics

Tests of Homoscedasticity, Normality, and Missing Completely at Random for Incomplete Multivariate Data

(2010) Mortaza Jamshidian et al.   PSYCHOMETRIKA