Prajit Rajkumar

TIMS-Bench evaluates three TIMS feature-finding tools using public and benchmark datasets, identifying strengths and weaknesses in feature detection and annotation.

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

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

This study evaluates imputation methods for untargeted metabolomics data, finding that kNN and RF imputation perform poorly with missing data, especially MNAR, and that missing data proportion significantly affects accuracy.

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.