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Inferring gene regression networks with model trees

BACKGROUND: Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically...

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Autores principales: Nepomuceno-Chamorro , Isabel A, Aguilar-Ruiz , Jesus S, Riquelme, Jose C
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2978221/
https://www.ncbi.nlm.nih.gov/pubmed/20950452
http://dx.doi.org/10.1186/1471-2105-11-517
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author Nepomuceno-Chamorro , Isabel A
Aguilar-Ruiz , Jesus S
Riquelme, Jose C
author_facet Nepomuceno-Chamorro , Isabel A
Aguilar-Ruiz , Jesus S
Riquelme, Jose C
author_sort Nepomuceno-Chamorro , Isabel A
collection PubMed
description BACKGROUND: Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities. RESULTS: We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named REGNET, is experimentally tested on two well-known data sets: Saccharomyces Cerevisiae and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database) is used as control to compare the results to that of a correlation-based method. This experiment shows that REGNET performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods. CONCLUSIONS: REGNET generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear regressions to separate areas of the search space favoring to infer localized similarities over a more global similarity. Furthermore, experimental results show the good performance of REGNET.
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spelling pubmed-29782212010-11-17 Inferring gene regression networks with model trees Nepomuceno-Chamorro , Isabel A Aguilar-Ruiz , Jesus S Riquelme, Jose C BMC Bioinformatics Methodology Article BACKGROUND: Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities. RESULTS: We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named REGNET, is experimentally tested on two well-known data sets: Saccharomyces Cerevisiae and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database) is used as control to compare the results to that of a correlation-based method. This experiment shows that REGNET performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods. CONCLUSIONS: REGNET generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear regressions to separate areas of the search space favoring to infer localized similarities over a more global similarity. Furthermore, experimental results show the good performance of REGNET. BioMed Central 2010-10-15 /pmc/articles/PMC2978221/ /pubmed/20950452 http://dx.doi.org/10.1186/1471-2105-11-517 Text en Copyright ©2010 Nepomuceno-Chamorro et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology Article
Nepomuceno-Chamorro , Isabel A
Aguilar-Ruiz , Jesus S
Riquelme, Jose C
Inferring gene regression networks with model trees
title Inferring gene regression networks with model trees
title_full Inferring gene regression networks with model trees
title_fullStr Inferring gene regression networks with model trees
title_full_unstemmed Inferring gene regression networks with model trees
title_short Inferring gene regression networks with model trees
title_sort inferring gene regression networks with model trees
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2978221/
https://www.ncbi.nlm.nih.gov/pubmed/20950452
http://dx.doi.org/10.1186/1471-2105-11-517
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