Ensemble inference and inferability of gene regulatory networks.
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 limi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2014-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4122380?pdf=render |
| _version_ | 1819148978481004544 |
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| author | S M Minhaz Ud-Dean Rudiyanto Gunawan |
| author_facet | S M Minhaz Ud-Dean Rudiyanto Gunawan |
| author_sort | S M Minhaz Ud-Dean |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T13:54:18Z |
| format | Article |
| id | doaj.art-8a55e13e9dc4443896872b9da7bcefe3 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-22T13:54:18Z |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-8a55e13e9dc4443896872b9da7bcefe32022-12-21T18:23:35ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0198e10381210.1371/journal.pone.0103812Ensemble inference and inferability of gene regulatory networks.S M Minhaz Ud-DeanRudiyanto GunawanThe 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.http://europepmc.org/articles/PMC4122380?pdf=render |
| spellingShingle | S M Minhaz Ud-Dean Rudiyanto Gunawan Ensemble inference and inferability of gene regulatory networks. PLoS ONE |
| title | Ensemble inference and inferability of gene regulatory networks. |
| title_full | Ensemble inference and inferability of gene regulatory networks. |
| title_fullStr | Ensemble inference and inferability of gene regulatory networks. |
| title_full_unstemmed | Ensemble inference and inferability of gene regulatory networks. |
| title_short | Ensemble inference and inferability of gene regulatory networks. |
| title_sort | ensemble inference and inferability of gene regulatory networks |
| url | http://europepmc.org/articles/PMC4122380?pdf=render |
| work_keys_str_mv | AT smminhazuddean ensembleinferenceandinferabilityofgeneregulatorynetworks AT rudiyantogunawan ensembleinferenceandinferabilityofgeneregulatorynetworks |