Metabolomics is an emerging field that quantifies numerous metabolites systematically. The
key purpose of metabolomics is to identify the metabolites corresponding to each biological …

Progress in mass spectrometry lipidomics has led to a rapid proliferation of studies across
biology and biomedicine. These generate extremely large raw datasets requiring …

Missing values exist widely in mass-spectrometry (MS) based metabolomics data. Various
methods have been applied for handling missing values, but the selection can significantly …

Background Non-targeted metabolomics based on mass spectrometry enables high-
throughput profiling of the metabolites in a biological sample. The large amount of data …

The predominance of partial least squares-discriminant analysis (PLS-DA) used to analyze
metabolomics datasets (indeed, it is the most well-known tool to perform classification and …

The life sciences are currently being transformed by an unprecedented wave of
developments in molecular analysis, which include important advances in instrumental …

BACKGROUND Nutritional metabolomics is rapidly evolving to integrate nutrition with
complex metabolomics data to discover new biomarkers of nutritional exposure and status …

Introduction The generic metabolomics data processing workflow is constructed with a serial
set of processes including peak picking, quality assurance, normalisation, missing value …

Introduction The field of metabolomics has expanded greatly over the past two decades,
both as an experimental science with applications in many areas, as well as in regards to …

Recently, numerous studies have been conducted on Missing Value Imputation (MVI),
intending the primary solution scheme for the datasets containing one or more missing …