Amy C. Harms

The paper analyzes longitudinal metabolomic changes of biogenic amines in plasma and CSF of TgF344 AD rats and WT controls, revealing sex- and age-specific differences.

This study identifies metabolic targets in breast cancer by analyzing multi-omics data from cell lines, revealing subtype-specific dependencies and potential drug targets.

The paper identifies reduced triacylglycerols and lipid droplets in resilient Alzheimer's donors compared to AD patients, using multi-omics analysis of brain tissue.

The paper identifies age, sex, and disease-dependent lipidomic changes in TgF344-AD rats, showing similarities to human lipid profiles and developing predictive models for AD.

This study identifies metabolic signatures associated with pulmonary embolism in COVID-19 patients using longitudinal ICU data, revealing key lipid and amine changes post-event.

mzQuality is a software tool for quality control in metabolomics data that corrects technical variation and prepares data for analysis.

This study evaluates normalization strategies for lipidomic data in cell line panels, finding that data-based approaches improve replicate similarity but may introduce artifacts.

fluxTrAM automates processing of tracer-based metabolomics data and integrates it with genome-scale metabolic models for flux analysis.

This paper introduces a new method for genome-scale metabolic flux inference using conserved moieties, demonstrating its computational efficiency and accuracy with isotopic labeling data.

The paper identifies a biomarker for in-ovo gender prediction in chicken eggs using mass spectrometry, achieving high sensitivity and specificity for female gender detection.

This study integrates genomics, epigenomics, and metabolomics data to identify ADHD biomarkers and explore omics-level connections, finding associations between PGSs, CpGs, and metabolites.

This study examines heritability of urinary metabolites in children, finding substantial genetic contributions across amines, organic acids, and steroids.

The paper develops genome-scale metabolic models of human dopaminergic neurons to study metabolic dysfunction in Parkinson's disease using exometabolomic and transcriptomic data.

The paper presents STATegra, a multi-omics dataset of mouse B-cell differentiation combining 10 omics technologies to study dynamic cellular processes.

This study estimates heritability and genetic contributions to metabolite levels using genome-wide data and twin-family cohorts, revealing class-specific genetic influences.