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Molecular basis of such communication has long been studied, but genome-wide screens for genes and other biomolecules mediating tissue-tissue signaling are lacking. To systematically identify inter-tissue mediators, we present a novel computational approach MultiCens (Multilayer\/Multi-tissue network Centrality measures). Unlike single-layer network methods, MultiCens can distinguish within- vs. across-layer connectivity to quantify the \u201cinfluence\u201d of any gene in a tissue on a query set of genes of interest in another tissue. MultiCens enjoys theoretical guarantees on convergence and decomposability, and performs well on synthetic benchmarks. On human multi-tissue datasets, MultiCens predicts known and novel genes linked to hormones. MultiCens further reveals shifts in gene network architecture among four brain regions in Alzheimer\u2019s disease. MultiCens-prioritized hypotheses from these two diverse applications, and potential future ones like \u201cMulti-tissue-expanded Gene Ontology\u201d analysis, can enable whole-body yet molecular-level systems investigations in humans.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1011022","type":"journal-article","created":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T13:38:02Z","timestamp":1682343482000},"page":"e1011022","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":13,"title":["MultiCens: Multilayer network centrality measures to uncover molecular mediators of tissue-tissue 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