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• W4385392710 abstract "Abstract Deciphering the functional organization of large biological networks is a major challenge for current mathematical methods. A common approach is to decompose networks into largely independent functional modules, but inferring these modules and their organization from network activity is difficult, given the uncertainties and in-completeness of measurements. Typically, some parts of the overall functional organization, such as intermediate processing steps, are latent. We show that the hidden structure can be uniquely determined from the statistical moments of observable network components alone, as long as the mean of each latent variable maps onto a scaled expectation of a binary variable and the functional relevance of the network components lies in their mean values. Whether the function of biological networks permits a hierarchical modularization can be falsified by a correlation-based statistical test that we derive. We apply the approach to gene regulatory networks, dendrites of pyramidal neurons, and networks of spiking neurons." @default.
• W4385392710 created "2023-07-30" @default.
• W4385392710 creator A5050101232 @default.
• W4385392710 date "2023-07-30" @default.
• W4385392710 modified "2023-09-27" @default.
• W4385392710 title "Correlations reveal the hierarchical organization of networks with latent binary variables" @default.
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• W4385392710 doi "https://doi.org/10.1101/2023.07.27.550891" @default.
• W4385392710 hasPublicationYear "2023" @default.
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