We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Correlations reveal the hierarchical organization of biological networks with latent variables.
- Authors
Häusler, Stefan
- 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 incompleteness 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 determined from the statistical moments of observable network components alone, as long as the functional relevance of the network components lies in their mean values and the mean of each latent variable maps onto a scaled expectation of a binary variable. 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 test to gene regulatory networks, dendrites of pyramidal neurons, and networks of spiking neurons. A method to infer the modular organization of biological networks based on incomplete information. The method is based solely on observable network data and can be used to yield reconstructions of networks obtained from different data sources, such as cellular, genetic and brain measurements.
- Subjects
BIOLOGICAL networks; LATENT variables; GENE regulatory networks; PYRAMIDAL neurons; HUMAN fingerprints
- Publication
Communications Biology, 2024, Vol 7, Issue 1, p1
- ISSN
2399-3642
- Publication type
Article
- DOI
10.1038/s42003-024-06342-y