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Discovering gene regulatory networks of multiple phenotypic groups using dynamic Bayesian networks
Dynamic Bayesian networks (DBNs) can be used for the discovery of gene regulatory networks (GRNs) from time series gene expression data. Here, we suggest a strategy for learning DBNs from gene expression data by employing a Bayesian approach that is scalable to large networks and is targeted at lear...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9294428/ https://www.ncbi.nlm.nih.gov/pubmed/35679575 http://dx.doi.org/10.1093/bib/bbac219 |
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author | Suter, Polina Kuipers, Jack Beerenwinkel, Niko |
author_facet | Suter, Polina Kuipers, Jack Beerenwinkel, Niko |
author_sort | Suter, Polina |
collection | PubMed |
description | Dynamic Bayesian networks (DBNs) can be used for the discovery of gene regulatory networks (GRNs) from time series gene expression data. Here, we suggest a strategy for learning DBNs from gene expression data by employing a Bayesian approach that is scalable to large networks and is targeted at learning models with high predictive accuracy. Our framework can be used to learn DBNs for multiple groups of samples and highlight differences and similarities in their GRNs. We learn these DBN models based on different structural and parametric assumptions and select the optimal model based on the cross-validated predictive accuracy. We show in simulation studies that our approach is better equipped to prevent overfitting than techniques used in previous studies. We applied the proposed DBN-based approach to two time series transcriptomic datasets from the Gene Expression Omnibus database, each comprising data from distinct phenotypic groups of the same tissue type. In the first case, we used DBNs to characterize responders and non-responders to anti-cancer therapy. In the second case, we compared normal to tumor cells of colorectal tissue. The classification accuracy reached by the DBN-based classifier for both datasets was higher than reported previously. For the colorectal cancer dataset, our analysis suggested that GRNs for cancer and normal tissues have a lot of differences, which are most pronounced in the neighborhoods of oncogenes and known cancer tissue markers. The identified differences in gene networks of cancer and normal cells may be used for the discovery of targeted therapies. |
format | Online Article Text |
id | pubmed-9294428 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-92944282022-07-20 Discovering gene regulatory networks of multiple phenotypic groups using dynamic Bayesian networks Suter, Polina Kuipers, Jack Beerenwinkel, Niko Brief Bioinform Problem Solving Protocol Dynamic Bayesian networks (DBNs) can be used for the discovery of gene regulatory networks (GRNs) from time series gene expression data. Here, we suggest a strategy for learning DBNs from gene expression data by employing a Bayesian approach that is scalable to large networks and is targeted at learning models with high predictive accuracy. Our framework can be used to learn DBNs for multiple groups of samples and highlight differences and similarities in their GRNs. We learn these DBN models based on different structural and parametric assumptions and select the optimal model based on the cross-validated predictive accuracy. We show in simulation studies that our approach is better equipped to prevent overfitting than techniques used in previous studies. We applied the proposed DBN-based approach to two time series transcriptomic datasets from the Gene Expression Omnibus database, each comprising data from distinct phenotypic groups of the same tissue type. In the first case, we used DBNs to characterize responders and non-responders to anti-cancer therapy. In the second case, we compared normal to tumor cells of colorectal tissue. The classification accuracy reached by the DBN-based classifier for both datasets was higher than reported previously. For the colorectal cancer dataset, our analysis suggested that GRNs for cancer and normal tissues have a lot of differences, which are most pronounced in the neighborhoods of oncogenes and known cancer tissue markers. The identified differences in gene networks of cancer and normal cells may be used for the discovery of targeted therapies. Oxford University Press 2022-06-10 /pmc/articles/PMC9294428/ /pubmed/35679575 http://dx.doi.org/10.1093/bib/bbac219 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Problem Solving Protocol Suter, Polina Kuipers, Jack Beerenwinkel, Niko Discovering gene regulatory networks of multiple phenotypic groups using dynamic Bayesian networks |
title | Discovering gene regulatory networks of multiple phenotypic groups using dynamic Bayesian networks |
title_full | Discovering gene regulatory networks of multiple phenotypic groups using dynamic Bayesian networks |
title_fullStr | Discovering gene regulatory networks of multiple phenotypic groups using dynamic Bayesian networks |
title_full_unstemmed | Discovering gene regulatory networks of multiple phenotypic groups using dynamic Bayesian networks |
title_short | Discovering gene regulatory networks of multiple phenotypic groups using dynamic Bayesian networks |
title_sort | discovering gene regulatory networks of multiple phenotypic groups using dynamic bayesian networks |
topic | Problem Solving Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9294428/ https://www.ncbi.nlm.nih.gov/pubmed/35679575 http://dx.doi.org/10.1093/bib/bbac219 |
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