Open access
Datum
2014-08-05Typ
- Journal Article
ETH Bibliographie
yes
Altmetrics
Abstract
The inference of gene regulatory network (GRN) from gene expression data is an unsolved problem of great importance. This inference has been stated, though not proven, to be underdetermined implying that there could be many equivalent (indistinguishable) solutions. Motivated by this fundamental limitation, we have developed new framework and algorithm, called TRaCE, for the ensemble inference of GRNs. The ensemble corresponds to the inherent uncertainty associated with discriminating direct and indirect gene regulations from steady-state data of gene knock-out (KO) experiments. We applied TRaCE to analyze the inferability of random GRNs and the GRNs of E. coli and yeast from single- and double-gene KO experiments. The results showed that, with the exception of networks with very few edges, GRNs are typically not inferable even when the data are ideal (unbiased and noise-free). Finally, we compared the performance of TRaCE with top performing methods of DREAM4 in silico network inference challenge. Mehr anzeigen
Persistenter Link
https://doi.org/10.3929/ethz-b-000090518Publikationsstatus
publishedExterne Links
Zeitschrift / Serie
PLoS ONEBand
Seiten / Artikelnummer
Verlag
PLOSOrganisationseinheit
03898 - Gunawan, Rudiyanto (ehemalig)
Förderung
137614 - Benchmarking, Assessment and Development of Methods for Biological Network Inference (SNF)
ETH Bibliographie
yes
Altmetrics