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Brief Bioinform. 2019 Jul 5. pii: bbz064. doi: 10.1093/bib/bbz064. [Epub ahead of print]

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

Author information

1
Science for Life Laboratory, Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Box 1031, 17121 Solna, Sweden.
2
Computational Cell Maps, Institute of Computational Biology, Helmholtz Center Munich, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany.

Abstract

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.

KEYWORDS:

Bayesian classification; functional association networks; network inference; protein–protein interactions

PMID:
31281921
DOI:
10.1093/bib/bbz064

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