Reference : Construction and contextualization approaches for protein-protein interaction networks.

	Document type :	Scientific journals : Article
	Discipline(s) :	Life sciences : Multidisciplinary, general & others
	To cite this reference:	http://hdl.handle.net/10993/51714

Title :	Construction and contextualization approaches for protein-protein interaction networks.
Language :	English
Author, co-author :	Badkas, Apurva [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Life Sciences and Medicine (DLSM)]
	de Landtsheer, Sébastien [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Life Sciences and Medicine (DLSM) >]
	Sauter, Thomas [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Life Sciences and Medicine (DLSM)]
Publication date :	2022
Journal title :	Computational and structural biotechnology journal
Volume :	20
Pages :	3280-3290
Peer reviewed :	Yes (verified by ORBilu)
Audience :	International
ISSN :	2001-0370
e-ISSN :	2001-0370
Keywords :	[en] Context-specific network ; Diffusion ; Neighborhood ; Protein-protein interaction network
Abstract :	[en] Protein-protein interaction network (PPIN) analysis is a widely used method to study the contextual role of proteins of interest, to predict novel disease genes, disease or functional modules, and to identify novel drug targets. PPIN-based analysis uses both generic and context-specific networks. Multiple contextualization methodologies have been described, such as shortest-path algorithms, neighborhood-based methods, and diffusion/propagation algorithms. This review discusses these methods, provides intuitive representations of PPIN contextualization, and also examines how the quality of such context-specific networks could be improved by considering additional sources of evidence. As a heuristic, we observe that tasks such as identifying disease genes, drug targets, and protein complexes should consider local neighborhoods, while uncovering disease mechanisms and discovering disease-pathways would gain from diffusion-based construction.
Permalink :	http://hdl.handle.net/10993/51714
Commentary :	© 2022 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

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