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Guala, D.* ; Ogris, C. ; Müller, N.S. ; Sonnhammer, E.L.L.*

Genome-wide functional association networks: Background, data & state-of-the-art resources.

Brief. Bioinform. 21, 1224-1237 (2020)
Verlagsversion DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
The vast amount of experimental data from recent advances in the field of high-throughput biology begs for integration into more complex data structures such as genome-wide functional association networks. Such networks have been used for elucidation of the interplay of intra-cellular molecules to make advances ranging from the basic science understanding of evolutionary processes to the more translational field of precision medicine. The allure of the field has resulted in rapid growth of the number of available network resources, each with unique attributes exploitable to answer different biological questions. Unfortunately, the high volume of network resources makes it impossible for the intended user to select an appropriate tool for their particular research question. The aim of this paper is to provide an overview of the underlying data and representative network resources as well as to mention methods of integration, allowing a customized approach to resource selection. Additionally, this report will provide a primer for researchers venturing into the field of network integration.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Functional Association Networks ; Network Inference ; Protein-protein Interactions ; Bayesian Classification; Protein-protein Interactions; Interaction Map; Gene Network; Data Sets; Prediction; Disease; Integration; Model; Scale; Identification
Sprache englisch
Veröffentlichungsjahr 2020
Prepublished im Jahr 2019
HGF-Berichtsjahr 2019
ISSN (print) / ISBN 1467-5463
e-ISSN 1477-4054
Zeitschrift Briefings in Bioinformatics
Quellenangaben Band: 21, Heft: 4, Seiten: 1224-1237 Artikelnummer: , Supplement: ,
Verlag Oxford University Press
Verlagsort Great Clarendon St, Oxford Ox2 6dp, England
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503800-001
Förderungen German Federal Ministry of Education and Research
Swedish Research Council
DOI 10.1093/bib/bbz064
WOS ID WOS:000576139100008
Scopus ID 85088264103
PubMed ID 31281921
Erfassungsdatum 2019-07-24
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