The Local Edge Machine: inference of dynamic models of gene regulation.

Published

Journal Article

We present a novel approach, the Local Edge Machine, for the inference of regulatory interactions directly from time-series gene expression data. We demonstrate its performance, robustness, and scalability on in silico datasets with varying behaviors, sizes, and degrees of complexity. Moreover, we demonstrate its ability to incorporate biological prior information and make informative predictions on a well-characterized in vivo system using data from budding yeast that have been synchronized in the cell cycle. Finally, we use an atlas of transcription data in a mammalian circadian system to illustrate how the method can be used for discovery in the context of large complex networks.

Full Text

Duke Authors

Cited Authors

  • McGoff, KA; Guo, X; Deckard, A; Kelliher, CM; Leman, AR; Francey, LJ; Hogenesch, JB; Haase, SB; Harer, JL

Published Date

  • October 19, 2016

Published In

Volume / Issue

  • 17 / 1

Start / End Page

  • 214 -

PubMed ID

  • 27760556

Pubmed Central ID

  • 27760556

Electronic International Standard Serial Number (EISSN)

  • 1474-760X

International Standard Serial Number (ISSN)

  • 1474-7596

Digital Object Identifier (DOI)

  • 10.1186/s13059-016-1076-z

Language

  • eng