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A pipeline to create predictive functional networks: application to the tumor progression of hepatocellular carcinoma
BACKGROUND: Integrating genome-wide gene expression patient profiles with regulatory knowledge is a challenging task because of the inherent heterogeneity, noise and incompleteness of biological data. From the computational side, several solvers for logic programs are able to perform extremely well...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6958715/ https://www.ncbi.nlm.nih.gov/pubmed/31937236 http://dx.doi.org/10.1186/s12859-019-3316-1 |
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author | Folschette, Maxime Legagneux, Vincent Poret, Arnaud Chebouba, Lokmane Guziolowski, Carito Théret, Nathalie |
author_facet | Folschette, Maxime Legagneux, Vincent Poret, Arnaud Chebouba, Lokmane Guziolowski, Carito Théret, Nathalie |
author_sort | Folschette, Maxime |
collection | PubMed |
description | BACKGROUND: Integrating genome-wide gene expression patient profiles with regulatory knowledge is a challenging task because of the inherent heterogeneity, noise and incompleteness of biological data. From the computational side, several solvers for logic programs are able to perform extremely well in decision problems for combinatorial search domains. The challenge then is how to process the biological knowledge in order to feed these solvers to gain insights in a biological study. It requires formalizing the biological knowledge to give a precise interpretation of this information; currently, very few pathway databases offer this possibility. RESULTS: The presented work proposes an automatic pipeline to extract automatically regulatory knowledge from pathway databases and generate novel computational predictions related to the state of expression or activity of biological molecules. We applied it in the context of hepatocellular carcinoma (HCC) progression, and evaluate the precision and the stability of these computational predictions. Our working base is a graph of 3383 nodes and 13,771 edges extracted from the KEGG database, in which we integrate 209 differentially expressed genes between low and high aggressive HCC across 294 patients. Our computational model predicts the shifts of expression of 146 initially non-observed biological components. Our predictions were validated at 88% using a larger experimental dataset and cross-validation techniques. In particular, we focus on the protein complexes predictions and show for the first time that NFKB1/BCL-3 complexes are activated in aggressive HCC. In spite of the large dimension of the reconstructed models, our analyses over the computational predictions discover a well constrained region where KEGG regulatory knowledge constrains gene expression of several biomolecules. These regions can offer interesting windows to perturb experimentally such complex systems. CONCLUSION: This new pipeline allows biologists to develop their own predictive models based on a list of genes. It facilitates the identification of new regulatory biomolecules using knowledge graphs and predictive computational methods. Our workflow is implemented in an automatic python pipeline which is publicly available at https://github.com/LokmaneChebouba/key-pipeand contains as testing data all the data used in this paper. |
format | Online Article Text |
id | pubmed-6958715 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-69587152020-01-17 A pipeline to create predictive functional networks: application to the tumor progression of hepatocellular carcinoma Folschette, Maxime Legagneux, Vincent Poret, Arnaud Chebouba, Lokmane Guziolowski, Carito Théret, Nathalie BMC Bioinformatics Research Article BACKGROUND: Integrating genome-wide gene expression patient profiles with regulatory knowledge is a challenging task because of the inherent heterogeneity, noise and incompleteness of biological data. From the computational side, several solvers for logic programs are able to perform extremely well in decision problems for combinatorial search domains. The challenge then is how to process the biological knowledge in order to feed these solvers to gain insights in a biological study. It requires formalizing the biological knowledge to give a precise interpretation of this information; currently, very few pathway databases offer this possibility. RESULTS: The presented work proposes an automatic pipeline to extract automatically regulatory knowledge from pathway databases and generate novel computational predictions related to the state of expression or activity of biological molecules. We applied it in the context of hepatocellular carcinoma (HCC) progression, and evaluate the precision and the stability of these computational predictions. Our working base is a graph of 3383 nodes and 13,771 edges extracted from the KEGG database, in which we integrate 209 differentially expressed genes between low and high aggressive HCC across 294 patients. Our computational model predicts the shifts of expression of 146 initially non-observed biological components. Our predictions were validated at 88% using a larger experimental dataset and cross-validation techniques. In particular, we focus on the protein complexes predictions and show for the first time that NFKB1/BCL-3 complexes are activated in aggressive HCC. In spite of the large dimension of the reconstructed models, our analyses over the computational predictions discover a well constrained region where KEGG regulatory knowledge constrains gene expression of several biomolecules. These regions can offer interesting windows to perturb experimentally such complex systems. CONCLUSION: This new pipeline allows biologists to develop their own predictive models based on a list of genes. It facilitates the identification of new regulatory biomolecules using knowledge graphs and predictive computational methods. Our workflow is implemented in an automatic python pipeline which is publicly available at https://github.com/LokmaneChebouba/key-pipeand contains as testing data all the data used in this paper. BioMed Central 2020-01-14 /pmc/articles/PMC6958715/ /pubmed/31937236 http://dx.doi.org/10.1186/s12859-019-3316-1 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Folschette, Maxime Legagneux, Vincent Poret, Arnaud Chebouba, Lokmane Guziolowski, Carito Théret, Nathalie A pipeline to create predictive functional networks: application to the tumor progression of hepatocellular carcinoma |
title | A pipeline to create predictive functional networks: application to the tumor progression of hepatocellular carcinoma |
title_full | A pipeline to create predictive functional networks: application to the tumor progression of hepatocellular carcinoma |
title_fullStr | A pipeline to create predictive functional networks: application to the tumor progression of hepatocellular carcinoma |
title_full_unstemmed | A pipeline to create predictive functional networks: application to the tumor progression of hepatocellular carcinoma |
title_short | A pipeline to create predictive functional networks: application to the tumor progression of hepatocellular carcinoma |
title_sort | pipeline to create predictive functional networks: application to the tumor progression of hepatocellular carcinoma |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6958715/ https://www.ncbi.nlm.nih.gov/pubmed/31937236 http://dx.doi.org/10.1186/s12859-019-3316-1 |
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