An ensemble learning approach to reverse-engineering transcriptional regulatory networks from time-series gene expression data
Open Access
- 1 January 2009
- journal article
- Published by Springer Nature in BMC Genomics
- Vol. 10 (Suppl 1) , S8
- https://doi.org/10.1186/1471-2164-10-s1-s8
Abstract
One of the most challenging tasks in the post-genomic era is to reconstruct the transcriptional regulatory networks. The goal is to reveal, for each gene that responds to a certain biological event, which transcription factors affect its expression, and how a set of transcription factors coordinate to accomplish temporal and spatial specific regulations.Keywords
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