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| Analisis Metabolomik Siri Masa× | Analisis Metabolomik Multi-Omik× | |
|---|---|---|
| Bidang | Bioinformatik | Bioinformatik |
| Keluarga | Process / pipeline | Process / pipeline |
| Tahun asal≠ | 2000s–2010s | 2000s–2010s (metabolomics ~2000; multi-omics integration ~2010s) |
| Pengasas≠ | Developed from general metabolomics workflows; longitudinal extensions pioneered by A. K. Smilde, R. Bino, and colleagues | Pioneered collectively; key early integrative frameworks by Nicholson & Lindon (metabolomics) and Hasin, Seldin & Lusis (multi-omics disease mapping) |
| Jenis≠ | Quantitative longitudinal omics pipeline | Integrative computational pipeline |
| Sumber perintis≠ | Smilde, A. K., van der Werf, M. J., Bijlsma, S., van der Werff-van der Vat, B. J. C., & Jellema, R. H. (2005). Fusion of mass spectrometry-based metabolomics data. Analytical Chemistry, 77(20), 6729–6736. link ↗ | Subramanian, I., Verma, S., Kumar, S., Jere, A., & Anamika, K. (2020). Multi-omics data integration, interpretation, and its application. Bioinformatics and Biology Insights, 14, 1177932219899051. link ↗ |
| Alias | longitudinal metabolomics, dynamic metabolomics, temporal metabolome profiling, kinetic metabolomics | metabolomics multi-omics integration, integrated metabolomics, multi-omics metabolite profiling, metabolome-centric multi-omics |
| Berkaitan≠ | 6 | 5 |
| Ringkasan≠ | Time-series metabolomics analysis profiles small-molecule metabolites from biological samples collected at multiple, ordered time points, enabling researchers to capture the dynamic flux of metabolic pathways in response to stimuli, disease progression, drug treatment, or developmental change. By integrating longitudinal statistical models with standard metabolomics preprocessing, the approach goes beyond a static metabolic snapshot to reveal how, when, and in what sequence metabolic responses unfold. | Multi-omics metabolomics analysis integrates metabolite profiling data — derived from mass spectrometry or NMR spectroscopy — with genomic, transcriptomic, and/or proteomic datasets to build a system-level view of biological phenotypes. By anchoring integration on the metabolome, which reflects the downstream functional output of gene expression and protein activity, this approach connects upstream molecular variation to observable biochemical states, enabling richer mechanistic insight than any single omics layer alone. |
| ScholarGateSet data ↗ |
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