Simon Rogers

This study aligns metabolomics LC-MS datasets from malaria, leishmaniasis, and Zika virus to identify fever-associated metabolites, finding the kynurenine pathway as most affected.

TopNEXt improves data acquisition in multi-sample LC-MS/MS metabolomics by extending DDA exclusion methods across samples using a Region of Interest and intensity-based scoring system.

This study benchmarks DDA and DIA acquisition methods in metabolomics using simulations and real-world validation, showing performance differences based on co-eluting ion counts.

The paper introduces a topic modeling method using LDA to detect gene expression changes in interacting cells from scRNA-seq data, applied to synthetic and real datasets.

The paper introduces NPLinker, a framework that combines scoring functions to link microbial genomic and metabolomic data, improving identification of biosynthetic gene clusters and their associated metabolites.

This paper develops new data-dependent acquisition strategies for metabolomics using an in silico framework, showing they fragment more unique ions than standard methods.

Spec2Vec improves spectral similarity scoring by learning fragmental relationships, showing better correlation with structural similarity than cosine-based scores in metabolomics analyses.

This paper introduces a probabilistic framework that integrates mass spectrometry and retention time data to improve small molecule identification in LC-MS experiments.

The paper introduces matchms, a tool for processing and evaluating similarity in mass spectrometry data, addressing challenges in dataset consistency and metadata quality.

The paper introduces FBMN, a method for improving molecular networking by enhancing quantitative analysis and isomer distinction using feature detection and alignment tools in the GNPS platform.

PALS ranks significantly changing metabolite sets across conditions, showing robustness to missing data in metabolomics analyses.

A web service renders tandem mass spectra held in metabolomics repositories as publication-ready figures and consistent exports, addressed through Universal Spectrum Identifiers extended to metabolomics.

The paper develops a statistical framework to evaluate mRNA biomarkers for DM1 treatment efficacy using machine learning and microarray data.

The paper introduces ViMMS, a simulator for LC-MS/MS fragmentation strategies, enabling in-silico testing and optimization of mass spectrometry acquisition methods.

This paper introduces methods to improve metabolite annotation using substructure discovery and machine learning, along with a database for shared motifs.

HDP-Align aligns peaks across multiple LC-MS runs using a Bayesian non-parametric model, providing confidence scores for alignment results.