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ComHub: Community predictions of hubs in gene regulatory networks

Authors
  • Åkesson, Julia1, 2
  • Lubovac-Pilav, Zelmina2
  • Magnusson, Rasmus1
  • Gustafsson, Mika1
  • 1 Linköping University, Linköping, 581 83, Sweden , Linköping (Sweden)
  • 2 University of Skövde, Skövde, 541 28, Sweden , Skövde (Sweden)
Type
Published Article
Journal
BMC Bioinformatics
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Feb 09, 2021
Volume
22
Issue
1
Identifiers
DOI: 10.1186/s12859-021-03987-y
Source
Springer Nature
Keywords
License
Green

Abstract

BackgroundHub transcription factors, regulating many target genes in gene regulatory networks (GRNs), play important roles as disease regulators and potential drug targets. However, while numerous methods have been developed to predict individual regulator-gene interactions from gene expression data, few methods focus on inferring these hubs.ResultsWe have developed ComHub, a tool to predict hubs in GRNs. ComHub makes a community prediction of hubs by averaging over predictions by a compendium of network inference methods. Benchmarking ComHub against the DREAM5 challenge data and two independent gene expression datasets showed a robust performance of ComHub over all datasets.ConclusionsIn contrast to other evaluated methods, ComHub consistently scored among the top performing methods on data from different sources. Lastly, we implemented ComHub to work with both predefined networks and to perform stand-alone network inference, which will make the method generally applicable.

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