Mingxun Wang

The paper expands a bile acid and steroidal MS/MS spectral library across three repositories, capturing diverse structures and providing a resource for classifying unknown spectra.

The paper introduces mzQC, a standard for representing quality control metrics in mass spectrometry, enabling interoperable and machine-readable QC reporting.

Polyethylene glycol in human biofluids can mark drug exposure rather than contamination; it is oxidized to acid metabolites and appears in 2.3% of public metabolomics files.

This paper integrates MALDI-MS/MS into a bioinformatics platform to enable rapid metabolite annotation and strain prioritization from microbial colonies.

The Everything Bagel feature finder automates metabolomics data processing, achieving comparable or better performance than existing methods with significantly reduced computational time.

IDBac provides a centralized database and platform for bacterial identification via MALDI-TOF MS, enabling spectral comparison and analysis of bacterial isolates.

The paper develops an agentic AI system using LLMs and domain tools to hypothesize and validate structures of unknown drug metabolites from mass spectrometry data.

The paper identifies alkamines as a previously hidden class of metabolites formed via reductive amination, revealing significant gaps in metabolomics and drug metabolism understanding.

This paper introduces MT-GEM and STEP to predict discrete structural transformations in metabolites from MS/MS spectra, achieving significant improvements over existing methods.

The paper systematically extracts and catalogs acylcarnitine metabolites from multiple repositories, identifying previously unknown carnitines and providing a resource for further metabolic research.

The paper introduces a Bayesian Personalized Ranking model to predict microbial substrate preferences and develops an agent that integrates this model with a growth model and LLM for autonomous microbial analysis.

This study maps conjugated metabolites by analyzing MS/MS spectra from public repositories, identifying potential conjugation events and providing a resource for structural discovery.

The paper introduces microbiomeMASST, a metadata network graph linking metabolites across datasets, enabling cross-study analysis of microbial metabolites and their biological context.

The paper develops a functional metabolomics framework using synthetic microbial communities and computational analysis to track drug metabolism by gut microbes, identifying metabolites and their correlations with microbial taxa.

The paper introduces a collaborative platform for curating microbial metabolite knowledge to enhance metabolomics data interpretation.

The paper uses synthetic multiplexing to annotate uncharacterized metabolites, identifying ibuprofen-carnitine as a metabolite linked to muscle toxicity.

IsoPairFinder identifies metabolic pathway intermediates by analyzing isotope-labeled and unlabeled metabolomics data from gene-edited microbes.

The paper introduces a deep learning model, ICEBERG, for structural elucidation using tandem mass spectrometry, achieving high accuracy in identifying molecular structures from complex mixtures.

The paper presents a multi-organ metabolomics atlas in AD mouse models, identifying dysregulated metabolites and microbiome associations linked to oxidative stress and cognitive impairment.

The paper characterizes a marine bacteria using a metabologenomics approach, linking genomic and metabolomic data to identify a new biosynthetic gene cluster and bioactive compound.

This paper introduces a mass spectrometry filtering tree to distinguish bile acid isomers in LC-MS/MS data and identifies a novel deoxycholyl-2-aminophenol amidate linked to whole grain consumption.

The paper creates a spectral library of N-acyl lipids from metabolomics data, identifies novel compounds, and links them to health conditions like HIV and cognitive impairment.

The paper develops a drug exposure resource from untargeted metabolomics data to identify medications without clinical records.

ModiFinder localizes structural modification sites in small molecules by aligning MS/MS spectra of known and unknown compounds.

The paper curates LC-MS/MS data from plant extracts to create a taxonomically focused database for exploring plant-derived molecules.

The paper creates a suspect spectral library for untargeted metabolomics by propagating annotations from public MS/MS spectra to improve interpretation of unannotated spectra.

BLINK rapidly computes cosine similarity scores for tandem mass spectra, achieving high accuracy and speed through sparse matrix operations.

The paper updates the spectrum_utils Python package to integrate mass spectrometry standards, enhance data processing, and unify visualization across proteomics and metabolomics applications.

NPOmix connects mass spectrometry fragmentation data to biosynthetic gene clusters using a k-nearest neighbor classifier, improving metabolite discovery and structure prediction.

The paper identifies and annotates heterogeneous multimeric metabolite ions in LC-MS data using labeled and unlabeled Pichia pastoris extracts and demonstrates their presence in public and additional datasets.