| Literature DB >> 29186304 |
Hairong Wei1,2.
Abstract
We have modified a multitude of transcription factors (TFs) in numerous plant species and some animal species, and obtained transgenic lines that exhibit phenotypic alterations. Whenever we observe phenotypic changes in a TF's transgenic lines, we are always eager to identify its target genes, collaborative regulators and even upstream high hierarchical regulators. This issue can be addressed by establishing a multilayered hierarchical gene regulatory network (ML-hGRN) centered around a given TF. In this article, a practical approach for constructing an ML-hGRN centered on a TF using a combined approach of top-down and bottom-up network construction methods is described. Strategies for constructing ML-hGRNs are vitally important, as these networks provide key information to advance our understanding of how biological processes are regulated.Keywords: backward elimination random forest; bottom-up graphic Gaussian model algorithm; multilayered hierarchical gene regulatory network; top-down graphic Gaussian Model algorithm; transcription factor
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Year: 2019 PMID: 29186304 DOI: 10.1093/bib/bbx152
Source DB: PubMed Journal: Brief Bioinform ISSN: 1467-5463 Impact factor: 11.622