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- W4366817264 abstract "With the evolution of multicellularity, communication among cells in different tissues and organs became pivotal to life. 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 influence 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's disease. MultiCens-prioritized hypotheses from these two diverse applications, and potential future ones like Multi-tissue-expanded Gene Ontology analysis, can enable whole-body yet molecular-level systems investigations in humans." @default.
- W4366817264 created "2023-04-25" @default.
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- W4366817264 date "2023-04-24" @default.
- W4366817264 modified "2023-10-14" @default.
- W4366817264 title "MultiCens: Multilayer network centrality measures to uncover molecular mediators of tissue-tissue communication" @default.
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- W4366817264 doi "https://doi.org/10.1371/journal.pcbi.1011022" @default.
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