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	Tidsserieanalys av "pathway"-anrikning – Dynamisk "pathway"-aktivitet över tid	Multi-omics Pathway Enrichment Analysis
Ämnesområde	Bioinformatik	Bioinformatik
Familj	Process / pipeline	Process / pipeline
Ursprungsår	2005–2014	2014–2016 (multi-omics extension of enrichment methods established ~2005)
Upphovsperson	Bar-Joseph and colleagues (temporal gene expression); extended by Cheng, Bhatt et al. for pathway-level time-series inference	Building on Subramanian et al. (2005); multi-omics integration formalised by Meng et al. and others (~2014–2016)
Typ	Functional enrichment analysis with temporal modeling	Integrative pathway analysis pipeline
Ursprungskälla	Ernst, J., Nau, G. J., & Bar-Joseph, Z. (2005). Clustering short time series gene expression data. Bioinformatics, 21(Suppl 1), i159–i168.	Meng, C., Kuster, B., Culhane, A. C., & Gholami, A. M. (2014). A multivariate approach to the integration of multi-omics datasets. BMC Bioinformatics, 15, 162.
Alias	temporal pathway analysis, longitudinal pathway enrichment, dynamic pathway analysis, TPEA	multi-omics pathway analysis, integrated pathway enrichment, multi-layer pathway enrichment, MOPEA
Närliggande	5	1
Sammanfattning	Time-series pathway enrichment analysis identifies biological pathways whose coordinated gene activity changes significantly across ordered time points. Rather than treating each time point independently, the method models the temporal trajectory of gene expression within each pathway and tests whether entire biological programs — not just individual genes — are activated or suppressed in a time-dependent manner. It is widely used in developmental biology, drug response studies, and infection time courses.	Multi-omics pathway enrichment analysis is a bioinformatics pipeline that integrates molecular data from two or more omics layers — such as transcriptomics, proteomics, metabolomics, and epigenomics — and tests whether the combined signal from those layers converges on specific biological pathways more than expected by chance. By considering multiple molecular levels simultaneously, it identifies pathway-level dysregulation that single-omics analyses would miss.