Gutenberg Open Science: Mining protein interactomes to improve their reliability and support the advancement of network medicine

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-7431

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DC Field	Value	Language
dc.contributor.author	Alanis-Lobato, Gregorio	-
dc.date.accessioned	2022-07-15T09:35:51Z	-
dc.date.available	2022-07-15T09:35:51Z	-
dc.date.issued	2015
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/7445	-
dc.description.abstract	High-throughput detection of protein interactions has had a major impact in our understanding of the intricate molecular machinery underlying the living cell, and has permitted the construction of very large protein interactomes. The protein networks that are currently available are incomplete and a significant percentage of their interactions are false positives. Fortunately, the structural properties observed in good quality social or technological networks are also present in biological systems. This has encouraged the development of tools, to improve the reliability of protein networks and predict new interactions based merely on the topological characteristics of their components. Since diseases are rarely caused by the malfunction of a single protein, having a more complete and reliable interactome is crucial in order to identify groups of inter-related proteins involved in disease etiology. These system components can then be targeted with minimal collateral damage. In this article, an important number of network mining tools is reviewed, together with resources from which reliable protein interactomes can be constructed. In addition to the review, a few representative examples of how molecular and clinical data can be integrated to deepen our understanding of pathogenesis are discussed.	en_GB
dc.description.sponsorship	DFG, Open Access-Publizieren Universität Mainz / Universitätsmedizin	de
dc.language.iso	eng	de
dc.rights	CC BY	*
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject.ddc	570 Biowissenschaften	de_DE
dc.subject.ddc	570 Life sciences	en_GB
dc.title	Mining protein interactomes to improve their reliability and support the advancement of network medicine	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-7431	-
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 10 Biologie	de
jgu.organisation.number	7970	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	Frontiers in genetics	de
jgu.journal.volume	6	de
jgu.pages.alternative	Art. 296	de
jgu.publisher.year	2015	-
jgu.publisher.name	Frontiers Media	de
jgu.publisher.place	Lausanne	de
jgu.publisher.uri	http://dx.doi.org/10.3389/fgene.2015.00296	de
jgu.publisher.issn	1664-8021	de
jgu.organisation.place	Mainz	-
jgu.identifier.pmid	26442112
jgu.subject.ddccode	570	de
opus.date.modified	2017-05-12T09:18:03Z
opus.subject.dfgcode	00-000
opus.organisation.string	FB 10: Biologie: Zentrum für Bioinformatik	de_DE
opus.identifier.opusid	52316
opus.importsource	pubmed
opus.institute.number	1010
opus.metadataonly	false
opus.type.contenttype	Keine	de_DE
opus.type.contenttype	None	en_EN
opus.affiliated	Alanis-Lobato, Gregorio
jgu.publisher.doi	10.3389/fgene.2015.00296	de
jgu.organisation.ror	https://ror.org/023b0x485	-
Appears in collections:	DFG-OA-Publizieren (2012 - 2017)

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