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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)
Publ. Version/Full Text DOI PMC
Open Access Gold (Paid Option)
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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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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
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
ISSN (print) / ISBN 1467-5463
e-ISSN 1477-4054
Journal Briefings in Bioinformatics
Quellenangaben Volume: 21, Issue: 4, Pages: 1224-1237 Article Number: , Supplement: ,
Publisher Oxford University Press
Publishing Place Great Clarendon St, Oxford Ox2 6dp, England
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Grants German Federal Ministry of Education and Research
Swedish Research Council