AI-driven gene-sets, networks, pathways, and interactions analyses of multi-omics data.

High-throughput omics technologies continue to expand the scale and dimensionality of biological datasets across research and clinical environments. Yet transforming these large molecular feature sets into mechanistic insight remains a central challenge in translational bioinformatics. Gene-set and pathway-based approaches address this need by providing biologically meaningful organizational units, such as processes, signaling modules, complexes, or phenotypic signatures, through which experimental signals can be interpreted. PAGER 3.0 represents a major advance in the PAGER knowledge ecosystem, offering an expanded and curated collection of Pathways, Annotated gene-lists and Gene signatures (PAGs), ontology-aware knowledge navigation, weighted enrichment analytics, and structured network-based prioritization tools. This chapter introduces the conceptual foundations of PAG-based analysis, details PAGER 3.0 architecture and analytical workflows, and demonstrates utility through a leukemia single-cell transcriptomics case study. Applications in systems biology, precision medicine, machine learning, and drug repurposing are also discussed.