Metabolomics and Mass Spectrometry Studies

This paper introduces a framework for large-scale dynamic metabolome drug profiling in mammalian cells, demonstrating its application to study dichloroacetate's toxic effects in ovarian cancer cell lines.

The paper evaluates the use of Orbitrap All Ion Fragmentation (AIF) analysis with the MetaboList R package for metabolite profiling in leukemia and melanoma samples, showing comparable manual and automated results.

This paper applies a Wisdom of Artificial Crowds (WoAC) strategy to feature selection in untargeted metabolomics for developing a blood-based diagnostic test for thrombotic myocardial infarction, achieving lower misclassification rates than Lasso and Elastic Net.

speaq 2.0 automates 1D NMR spectral processing and quantification, reducing user interaction and enabling reproducible metabolomics analysis.

GSimp is a Gibbs sampler-based method for imputing left-censored missing values in metabolomics data, showing superior performance in accuracy and statistical analysis.

The paper compares imputation methods for metabolomics data with missing values and proposes a strategy with a web-tool.

R2DGC aligns and identifies peaks in 2D gas chromatography-mass spectrometry data without thresholds, using R.

Curatr is a web application that enables the manual curation of mass spectral libraries from LC-MS data, supporting export to standard formats and public access.

The paper develops methods to estimate false discovery rates in untargeted metabolomics annotations and shows how adjusting spectral matching parameters can significantly increase the number of annotations.

This study compares deep learning and machine learning methods for classifying estrogen receptor status in breast cancer metabolomics data, finding deep learning achieves higher accuracy.

The paper compares metabolomic profiles of diabetic and healthy dogs to identify similarities with human type 1 diabetes.

The paper reanalyzes five untargeted metabolomics studies and reports significant omissions and errors in their data analysis.

The paper evaluates classifier performance for multiclass phenotype discrimination in untargeted metabolomics using simulated and real data, finding that SVM and Random Forest outperform PLS-DA and Neural Networks in most scenarios.