Name
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1186/s12859-021-03987-y | BMC BIOINFORMATICS |
Keywords | DocType | Volume |
Gene regulatory networks, Hubs, Master regulators, Network inference | Journal | 22 |
Issue | ISSN | Citations |
1 | 1471-2105 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Julia Åkesson | 1 | 0 | 0.34 |
| Zelmina Lubovac-Pilav | 2 | 0 | 1.69 |
| Rasmus Magnusson | 3 | 1 | 1.37 |
| Mika Gustafsson | 4 | 0 | 0.34 |