Modelling Self-Organization in Complex Networks Via a Brain-Inspired Network Automata Theory Improves Link Reliability in Protein Interactomes

Abstract Protein interactomes are epitomes of incomplete and noisy networks. Methods for assessing link-reliability using exclusively topology are valuable in network biology, and their investigation facilitates the general understanding of topological mechanisms and models to draw and correct compl...

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Autor principal:	Carlo Vittorio Cannistraci
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Link Reliability Common Neighbors (CN) Network Biology Area Under The Precision-recall Curve (AUPR) Link Prediction Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5b7bde5d255c48c2a2e97ac6e55d0280
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spelling	oai:doaj.org-article:5b7bde5d255c48c2a2e97ac6e55d02802021-12-02T11:40:24ZModelling Self-Organization in Complex Networks Via a Brain-Inspired Network Automata Theory Improves Link Reliability in Protein Interactomes10.1038/s41598-018-33576-82045-2322https://doaj.org/article/5b7bde5d255c48c2a2e97ac6e55d02802018-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-33576-8https://doaj.org/toc/2045-2322Abstract Protein interactomes are epitomes of incomplete and noisy networks. Methods for assessing link-reliability using exclusively topology are valuable in network biology, and their investigation facilitates the general understanding of topological mechanisms and models to draw and correct complex network connectivity. Here, I revise and extend the local-community-paradigm (LCP). Initially detected in brain-network topological self-organization and afterward generalized to any complex network, the LCP is a theory to model local-topology-dependent link-growth in complex networks using network automata. Four novel LCP-models are compared versus baseline local-topology-models. It emerges that the reliability of an interaction between two proteins is higher: (i) if their common neighbours are isolated in a complex (local-community) that has low tendency to interact with other external proteins; (ii) if they have a low propensity to link with other proteins external to the local-community. These two rules are mathematically combined in C1*: a proposed mechanistic model that, in fact, outperforms the others. This theoretical study elucidates basic topological rules behind self-organization principia of protein interactomes and offers the conceptual basis to extend this theory to any class of complex networks. The link-reliability improvement, based on the mere topology, can impact many applied domains such as systems biology and network medicine.Carlo Vittorio CannistraciNature PortfolioarticleLink ReliabilityCommon Neighbors (CN)Network BiologyArea Under The Precision-recall Curve (AUPR)Link PredictionMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-16 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Link Reliability Common Neighbors (CN) Network Biology Area Under The Precision-recall Curve (AUPR) Link Prediction Medicine R Science Q
spellingShingle	Link Reliability Common Neighbors (CN) Network Biology Area Under The Precision-recall Curve (AUPR) Link Prediction Medicine R Science Q Carlo Vittorio Cannistraci Modelling Self-Organization in Complex Networks Via a Brain-Inspired Network Automata Theory Improves Link Reliability in Protein Interactomes
description	Abstract Protein interactomes are epitomes of incomplete and noisy networks. Methods for assessing link-reliability using exclusively topology are valuable in network biology, and their investigation facilitates the general understanding of topological mechanisms and models to draw and correct complex network connectivity. Here, I revise and extend the local-community-paradigm (LCP). Initially detected in brain-network topological self-organization and afterward generalized to any complex network, the LCP is a theory to model local-topology-dependent link-growth in complex networks using network automata. Four novel LCP-models are compared versus baseline local-topology-models. It emerges that the reliability of an interaction between two proteins is higher: (i) if their common neighbours are isolated in a complex (local-community) that has low tendency to interact with other external proteins; (ii) if they have a low propensity to link with other proteins external to the local-community. These two rules are mathematically combined in C1*: a proposed mechanistic model that, in fact, outperforms the others. This theoretical study elucidates basic topological rules behind self-organization principia of protein interactomes and offers the conceptual basis to extend this theory to any class of complex networks. The link-reliability improvement, based on the mere topology, can impact many applied domains such as systems biology and network medicine.
format	article
author	Carlo Vittorio Cannistraci
author_facet	Carlo Vittorio Cannistraci
author_sort	Carlo Vittorio Cannistraci
title	Modelling Self-Organization in Complex Networks Via a Brain-Inspired Network Automata Theory Improves Link Reliability in Protein Interactomes
title_short	Modelling Self-Organization in Complex Networks Via a Brain-Inspired Network Automata Theory Improves Link Reliability in Protein Interactomes
title_full	Modelling Self-Organization in Complex Networks Via a Brain-Inspired Network Automata Theory Improves Link Reliability in Protein Interactomes
title_fullStr	Modelling Self-Organization in Complex Networks Via a Brain-Inspired Network Automata Theory Improves Link Reliability in Protein Interactomes
title_full_unstemmed	Modelling Self-Organization in Complex Networks Via a Brain-Inspired Network Automata Theory Improves Link Reliability in Protein Interactomes
title_sort	modelling self-organization in complex networks via a brain-inspired network automata theory improves link reliability in protein interactomes
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/5b7bde5d255c48c2a2e97ac6e55d0280
work_keys_str_mv	AT carlovittoriocannistraci modellingselforganizationincomplexnetworksviaabraininspirednetworkautomatatheoryimproveslinkreliabilityinproteininteractomes
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Modelling Self-Organization in Complex Networks Via a Brain-Inspired Network Automata Theory Improves Link Reliability in Protein Interactomes

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