
AbstractSummaryHub 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. We 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 to the DREAM5 challenge data and an independent data set of human gene expression, proved a robust performance of ComHub over all data sets. Lastly, we implemented ComHub to work with both predefined networks and to do standard network inference, which we believe will make it generally applicable.AvailabilityCode is available at https://gitlab.com/Gustafsson-lab/comhubContactmika.gustafsson@liu.se, rasmus.magnusson@liu.se
Bioinformatics and Systems Biology, QH301-705.5, Methodology Article, Computer applications to medicine. Medical informatics, Bioinformatics and Computational Biology, R858-859.7, Computational Biology, Gene Expression, Bioinformatik och systembiologi, Network inference, Gene regulatory networks, Bioinformatik och beräkningsbiologi, Hubs, Gene regulatory networks; Hubs; Master regulators; Network inference, Master regulators, Gene Regulatory Networks, Biology (General), Algorithms, Transcription Factors
Bioinformatics and Systems Biology, QH301-705.5, Methodology Article, Computer applications to medicine. Medical informatics, Bioinformatics and Computational Biology, R858-859.7, Computational Biology, Gene Expression, Bioinformatik och systembiologi, Network inference, Gene regulatory networks, Bioinformatik och beräkningsbiologi, Hubs, Gene regulatory networks; Hubs; Master regulators; Network inference, Master regulators, Gene Regulatory Networks, Biology (General), Algorithms, Transcription Factors
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 11 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
