Authors:	Alanis-Lobato, Gregorio
Title:	Mining protein interactomes to improve their reliability and support the advancement of network medicine
Online publication date:	15-Jul-2022
Year of first publication:	2015
Language:	english
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.
DDC:	570 Biowissenschaften 570 Life sciences
Institution:	Johannes Gutenberg-Universität Mainz
Department:	FB 10 Biologie
Place:	Mainz
ROR:	https://ror.org/023b0x485
DOI:	http://doi.org/10.25358/openscience-7431
Version:	Published version
Publication type:	Zeitschriftenaufsatz
License:	CC BY
Information on rights of use:	https://creativecommons.org/licenses/by/4.0/
Journal:	Frontiers in genetics 6
Pages or article number:	Art. 296
Publisher:	Frontiers Media
Publisher place:	Lausanne
Issue date:	2015
ISSN:	1664-8021
Publisher URL:	http://dx.doi.org/10.3389/fgene.2015.00296
Publisher DOI:	10.3389/fgene.2015.00296
Appears in collections:	DFG-OA-Publizieren (2012 - 2017)