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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 Gold (Paid Option)
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
Korrespondenzautor
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
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
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Förderungen German Federal Ministry of Education and Research
Swedish Research Council