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Improving gene regulatory network inference and assessment: The importance of using network structure

Gene regulatory networks are graph models representing cellular transcription events. Networks are far from complete due to time and resource consumption for experimental validation and curation of the interactions. Previous assessments have shown the modest performance of the available network infe...

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Autores principales: Escorcia-Rodríguez, Juan M., Gaytan-Nuñez, Estefani, Hernandez-Benitez, Ericka M., Zorro-Aranda, Andrea, Tello-Palencia, Marco A., Freyre-González, Julio A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10012345/
https://www.ncbi.nlm.nih.gov/pubmed/36926589
http://dx.doi.org/10.3389/fgene.2023.1143382
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author Escorcia-Rodríguez, Juan M.
Gaytan-Nuñez, Estefani
Hernandez-Benitez, Ericka M.
Zorro-Aranda, Andrea
Tello-Palencia, Marco A.
Freyre-González, Julio A.
author_facet Escorcia-Rodríguez, Juan M.
Gaytan-Nuñez, Estefani
Hernandez-Benitez, Ericka M.
Zorro-Aranda, Andrea
Tello-Palencia, Marco A.
Freyre-González, Julio A.
author_sort Escorcia-Rodríguez, Juan M.
collection PubMed
description Gene regulatory networks are graph models representing cellular transcription events. Networks are far from complete due to time and resource consumption for experimental validation and curation of the interactions. Previous assessments have shown the modest performance of the available network inference methods based on gene expression data. Here, we study several caveats on the inference of regulatory networks and methods assessment through the quality of the input data and gold standard, and the assessment approach with a focus on the global structure of the network. We used synthetic and biological data for the predictions and experimentally-validated biological networks as the gold standard (ground truth). Standard performance metrics and graph structural properties suggest that methods inferring co-expression networks should no longer be assessed equally with those inferring regulatory interactions. While methods inferring regulatory interactions perform better in global regulatory network inference than co-expression-based methods, the latter is better suited to infer function-specific regulons and co-regulation networks. When merging expression data, the size increase should outweigh the noise inclusion and graph structure should be considered when integrating the inferences. We conclude with guidelines to take advantage of inference methods and their assessment based on the applications and available expression datasets.
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spelling pubmed-100123452023-03-15 Improving gene regulatory network inference and assessment: The importance of using network structure Escorcia-Rodríguez, Juan M. Gaytan-Nuñez, Estefani Hernandez-Benitez, Ericka M. Zorro-Aranda, Andrea Tello-Palencia, Marco A. Freyre-González, Julio A. Front Genet Genetics Gene regulatory networks are graph models representing cellular transcription events. Networks are far from complete due to time and resource consumption for experimental validation and curation of the interactions. Previous assessments have shown the modest performance of the available network inference methods based on gene expression data. Here, we study several caveats on the inference of regulatory networks and methods assessment through the quality of the input data and gold standard, and the assessment approach with a focus on the global structure of the network. We used synthetic and biological data for the predictions and experimentally-validated biological networks as the gold standard (ground truth). Standard performance metrics and graph structural properties suggest that methods inferring co-expression networks should no longer be assessed equally with those inferring regulatory interactions. While methods inferring regulatory interactions perform better in global regulatory network inference than co-expression-based methods, the latter is better suited to infer function-specific regulons and co-regulation networks. When merging expression data, the size increase should outweigh the noise inclusion and graph structure should be considered when integrating the inferences. We conclude with guidelines to take advantage of inference methods and their assessment based on the applications and available expression datasets. Frontiers Media S.A. 2023-02-27 /pmc/articles/PMC10012345/ /pubmed/36926589 http://dx.doi.org/10.3389/fgene.2023.1143382 Text en Copyright © 2023 Escorcia-Rodríguez, Gaytan-Nuñez, Hernandez-Benitez, Zorro-Aranda, Tello-Palencia and Freyre-González. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Escorcia-Rodríguez, Juan M.
Gaytan-Nuñez, Estefani
Hernandez-Benitez, Ericka M.
Zorro-Aranda, Andrea
Tello-Palencia, Marco A.
Freyre-González, Julio A.
Improving gene regulatory network inference and assessment: The importance of using network structure
title Improving gene regulatory network inference and assessment: The importance of using network structure
title_full Improving gene regulatory network inference and assessment: The importance of using network structure
title_fullStr Improving gene regulatory network inference and assessment: The importance of using network structure
title_full_unstemmed Improving gene regulatory network inference and assessment: The importance of using network structure
title_short Improving gene regulatory network inference and assessment: The importance of using network structure
title_sort improving gene regulatory network inference and assessment: the importance of using network structure
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10012345/
https://www.ncbi.nlm.nih.gov/pubmed/36926589
http://dx.doi.org/10.3389/fgene.2023.1143382
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