iDMET

iDMET integrates differential metabolomic profiles across heterogeneous studies using a network-based approach to identify metabolic alterations relevant to cancer research.

Key Features:

• Integration of heterogeneous data: iDMET integrates metabolomic data from diverse studies generated using different sample types, facilities, and measurement techniques to accommodate variability in target metabolites and detection sensitivities.
• Network-based approach: iDMET constructs networks by connecting differential metabolic changes observed between two groups (e.g., diseased vs. healthy) across studies, enabling integration based on functional relationships rather than raw metabolite-level comparisons.
• Cross-study association discovery: iDMET analyzes the integrated network to reveal novel associations, including potential connections between drugs that have similar metabolic impacts.

Scientific Applications:

• Cancer metabolomics integration: integration of metabolomic data from 27 published studies to characterize metabolic alterations associated with cancer.
• Biomarker and therapeutic-target identification: connecting differential changes across studies to support identification of potential biomarkers and therapeutic targets.
• Drug–metabolism association analysis: detecting potential links between drugs and their metabolic effects across datasets.

Methodology:

Data collection: gathering metabolomic data from published studies; Network construction: building a network from differential profiles between two groups to link similar metabolic changes across datasets; Network analysis: analyzing the constructed network to identify associations such as drug effects on metabolic pathways.