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Connectivity in the Yeast Cell Cycle Transcription Network: Inferences from Neural Networks
A current challenge is to develop computational approaches to infer gene network regulatory relationships based on multiple types of large-scale functional genomic data. We find that single-layer feed-forward artificial neural network (ANN) models can effectively discover gene network structure by i...
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Formato: | Texto |
Lenguaje: | English |
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Public Library of Science
2006
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1761652/ https://www.ncbi.nlm.nih.gov/pubmed/17194216 http://dx.doi.org/10.1371/journal.pcbi.0020169 |
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author | Hart, Christopher E Mjolsness, Eric Wold, Barbara J |
author_facet | Hart, Christopher E Mjolsness, Eric Wold, Barbara J |
author_sort | Hart, Christopher E |
collection | PubMed |
description | A current challenge is to develop computational approaches to infer gene network regulatory relationships based on multiple types of large-scale functional genomic data. We find that single-layer feed-forward artificial neural network (ANN) models can effectively discover gene network structure by integrating global in vivo protein:DNA interaction data (ChIP/Array) with genome-wide microarray RNA data. We test this on the yeast cell cycle transcription network, which is composed of several hundred genes with phase-specific RNA outputs. These ANNs were robust to noise in data and to a variety of perturbations. They reliably identified and ranked 10 of 12 known major cell cycle factors at the top of a set of 204, based on a sum-of-squared weights metric. Comparative analysis of motif occurrences among multiple yeast species independently confirmed relationships inferred from ANN weights analysis. ANN models can capitalize on properties of biological gene networks that other kinds of models do not. ANNs naturally take advantage of patterns of absence, as well as presence, of factor binding associated with specific expression output; they are easily subjected to in silico “mutation” to uncover biological redundancies; and they can use the full range of factor binding values. A prominent feature of cell cycle ANNs suggested an analogous property might exist in the biological network. This postulated that “network-local discrimination” occurs when regulatory connections (here between MBF and target genes) are explicitly disfavored in one network module (G2), relative to others and to the class of genes outside the mitotic network. If correct, this predicts that MBF motifs will be significantly depleted from the discriminated class and that the discrimination will persist through evolution. Analysis of distantly related Schizosaccharomyces pombe confirmed this, suggesting that network-local discrimination is real and complements well-known enrichment of MBF sites in G1 class genes. |
format | Text |
id | pubmed-1761652 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2006 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-17616522007-01-05 Connectivity in the Yeast Cell Cycle Transcription Network: Inferences from Neural Networks Hart, Christopher E Mjolsness, Eric Wold, Barbara J PLoS Comput Biol Research Article A current challenge is to develop computational approaches to infer gene network regulatory relationships based on multiple types of large-scale functional genomic data. We find that single-layer feed-forward artificial neural network (ANN) models can effectively discover gene network structure by integrating global in vivo protein:DNA interaction data (ChIP/Array) with genome-wide microarray RNA data. We test this on the yeast cell cycle transcription network, which is composed of several hundred genes with phase-specific RNA outputs. These ANNs were robust to noise in data and to a variety of perturbations. They reliably identified and ranked 10 of 12 known major cell cycle factors at the top of a set of 204, based on a sum-of-squared weights metric. Comparative analysis of motif occurrences among multiple yeast species independently confirmed relationships inferred from ANN weights analysis. ANN models can capitalize on properties of biological gene networks that other kinds of models do not. ANNs naturally take advantage of patterns of absence, as well as presence, of factor binding associated with specific expression output; they are easily subjected to in silico “mutation” to uncover biological redundancies; and they can use the full range of factor binding values. A prominent feature of cell cycle ANNs suggested an analogous property might exist in the biological network. This postulated that “network-local discrimination” occurs when regulatory connections (here between MBF and target genes) are explicitly disfavored in one network module (G2), relative to others and to the class of genes outside the mitotic network. If correct, this predicts that MBF motifs will be significantly depleted from the discriminated class and that the discrimination will persist through evolution. Analysis of distantly related Schizosaccharomyces pombe confirmed this, suggesting that network-local discrimination is real and complements well-known enrichment of MBF sites in G1 class genes. Public Library of Science 2006-12 2006-12-22 /pmc/articles/PMC1761652/ /pubmed/17194216 http://dx.doi.org/10.1371/journal.pcbi.0020169 Text en © 2006 Hart et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Hart, Christopher E Mjolsness, Eric Wold, Barbara J Connectivity in the Yeast Cell Cycle Transcription Network: Inferences from Neural Networks |
title | Connectivity in the Yeast Cell Cycle Transcription Network: Inferences from Neural Networks |
title_full | Connectivity in the Yeast Cell Cycle Transcription Network: Inferences from Neural Networks |
title_fullStr | Connectivity in the Yeast Cell Cycle Transcription Network: Inferences from Neural Networks |
title_full_unstemmed | Connectivity in the Yeast Cell Cycle Transcription Network: Inferences from Neural Networks |
title_short | Connectivity in the Yeast Cell Cycle Transcription Network: Inferences from Neural Networks |
title_sort | connectivity in the yeast cell cycle transcription network: inferences from neural networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1761652/ https://www.ncbi.nlm.nih.gov/pubmed/17194216 http://dx.doi.org/10.1371/journal.pcbi.0020169 |
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