Metabolomics and Mass Spectrometry Studies

Incorporating in-source fragment information improves metabolite identification accuracy in untargeted LC-MS datasets

The paper improves metabolite identification in LC-MS datasets by incorporating in-source fragment information from MS/MS spectral libraries.

Unsupervised integration of multimodal dataset identifies novel signatures of health and disease

The paper integrates multimodal health data to identify novel biomarker signatures for health and disease, showing better predictive power for diabetes and hypertension than traditional biomarkers.

Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies

The paper evaluates missing data handling strategies in untargeted metabolomics data, finding that KNN imputation with variable pre-selection is robust across multiple evaluation schemes.

High Peak Density Artifacts in Fourier Transform Mass Spectra and their Effects on Data Analysis

The paper identifies and addresses high peak density artifacts in Fourier transform mass spectra, which can mislead data analysis by creating false peaks.

Data-adaptive pipeline for filtering and normalizing metabolomics data.

The paper introduces a data-adaptive pipeline for filtering and normalizing metabolomics data, showing it outperforms traditional methods in reducing technical noise and preserving biological signals.

Inferring metabolite interactomes via molecular structure informed Bayesian graphical model selection with an application to coronary artery disease

The paper develops a Bayesian graphical model to infer metabolite interactomes using molecular structure information and applies it to coronary artery disease data.

A framework for large-scale dynamic metabolome drug profiling in mammalian cells: a case study analysis of the anti-cancer drug dichloroacetate

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.

simExTargId: An R package for real-time LC-MS metabolomic data analysis, instrument failure/drift notification and MS2 target identification

A Survey of Orbitrap All Ion Fragmentation Analysis Assessed by an R MetaboList Package to Study Small-molecule Metabolites

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.

DIMEdb: an integrated database and web service for metabolite identification in direct infusion mass spectrometery

DIMEdb provides a unified web-based database for metabolite identification in direct infusion mass spectrometry, integrating over 80,000 metabolites from multiple sources.

Wisdom of artificial crowds feature selection in untargeted metabolomics: An application to the development of a blood-based diagnostic test for thrombotic myocardial infarction

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: a complete workflow for high-throughput 1D NMR spectra processing and quantification

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

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

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

Quality control-based signal drift correction and interpretations of metabolomics/proteomics data using random forest regression

The paper develops a quality control-based random forest regression method to correct signal drift in metabolomics and proteomics data, improving precision and statistical accuracy.

Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data

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

R2DGC: Threshold-free peak alignment and identification for 2D gas chromatography mass spectrometry in R

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

Curatr: a web application for creating, curating, and sharing a mass spectral library

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.

Significance estimation for large scale untargeted metabolomics annotations

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.

Deep learning accurately predicts estrogen receptor status in breast cancer metabolomics data

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.

Differential Impacts of Dietary Modification on Individual Metabolic Phenotypes and their Relationship to Blood Pressure: Evidence of Latent Dietary Responder Sub-phenotypes

The paper identifies urinary metabolic phenotypes associated with three diets and their relationship to blood pressure, revealing diet-specific biomarkers and suggesting individualized dietary approaches.

Untargeted Metabolomic Analysis In Naturally Occurring Canine Diabetes Mellitus Identifies Similarities To Human Type 1 Diabetes

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

Untargeted Metabolomics Suffers From Incomplete Data Analysis

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

Evaluation Of Classifier Performance For Multiclass Phenotype Discrimination In Untargeted Metabolomics

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.