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Genome-wide functional association networks: Background, data & state-of-the-art resources.
Brief. Bioinform. 21, 1224-1237 (2020)
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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8.990
2.126
3
9
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
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
Language
english
Publication Year
2020
Prepublished in Year
2019
HGF-reported in Year
2019
ISSN (print) / ISBN
1467-5463
e-ISSN
1477-4054
Journal
Briefings in Bioinformatics
Quellenangaben
Volume: 21,
Issue: 4,
Pages: 1224-1237
Publisher
Oxford University Press
Publishing Place
Great Clarendon St, Oxford Ox2 6dp, England
Reviewing status
Peer reviewed
Institute(s)
Institute of Computational Biology (ICB)
POF-Topic(s)
30205 - Bioengineering and Digital Health
Research field(s)
Enabling and Novel Technologies
PSP Element(s)
G-503800-001
Grants
German Federal Ministry of Education and Research
Swedish Research Council
Swedish Research Council
WOS ID
WOS:000576139100008
Scopus ID
85088264103
PubMed ID
31281921
Erfassungsdatum
2019-07-24