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Hawe, J. ; Theis, F.J. ; Heinig, M.

Inferring interaction networks from multi-omics data.

Front. Genet. 10:535 (2019)
Publ. Version/Full Text DOI
Open Access Gold
Creative Commons Lizenzvertrag
A major goal in systems biology is a comprehensive description of the entirety of all complex interactions between different types of biomolecules-also referred to as the interactome-and how these interactions give rise to higher, cellular and organism level functions or diseases. Numerous efforts have been undertaken to define such interactomes experimentally, for example yeast-two-hybrid based protein-protein interaction networks or ChIP-seq based protein-DNA interactions for individual proteins. To complement these direct measurements, genome-scale quantitative multi-omics data (transcriptomics, proteomics, metabolomics, etc.) enable researchers to predict novel functional interactions between molecular species. Moreover, these data allow to distinguish relevant functional from non-functional interactions in specific biological contexts. However, integration of multi-omics data is not straight forward due to their heterogeneity. Numerous methods for the inference of interaction networks from homogeneous functional data exist, but with the advent of large-scale paired multi-omics data a new class of methods for inferring comprehensive networks across different molecular species began to emerge. Here we review state-of-the-art techniques for inferring the topology of interaction networks from functional multi-omics data, encompassing graphical models with multiple node types and quantitative-trait-loci (QTL) based approaches. In addition, we will discuss Bayesian aspects of network inference, which allow for leveraging already established biological information such as known protein-protein or protein-DNA interactions, to guide the inference process.
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Publication type Article: Journal article
Document type Review
Corresponding Author
Keywords Data Integration ; Genomics ; Machine Learning ; Mixed Data ; Personalized Medicine ; Prior Information ; Single Cell ; Systems Biology; Gene Network; Regulatory Network; Integrative Analysis; Variable Selection; Rna Interactions; Expression; Reconstruction; Association; Transcription; Encyclopedia
ISSN (print) / ISBN 1664-8021
e-ISSN 1664-8021
Journal Frontiers in Genetics
Quellenangaben Volume: 10, Issue: , Pages: , Article Number: 535 Supplement: ,
Publisher Frontiers
Publishing Place Avenue Du Tribunal Federal 34, Lausanne, Ch-1015, Switzerland
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)