A pipeline to create predictive functional networks: application to the tumor progression of hepatocellular carcinoma
Abstract 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 extreme...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2020-01-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-019-3316-1 |
| _version_ | 1818383074603302912 |
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| author | Maxime Folschette Vincent Legagneux Arnaud Poret Lokmane Chebouba Carito Guziolowski Nathalie Théret |
| author_facet | Maxime Folschette Vincent Legagneux Arnaud Poret Lokmane Chebouba Carito Guziolowski Nathalie Théret |
| author_sort | Maxime Folschette |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-14T03:00:35Z |
| format | Article |
| id | doaj.art-8853508052934fa38cea3be7db971abe |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-14T03:00:35Z |
| publishDate | 2020-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-8853508052934fa38cea3be7db971abe2022-12-21T23:19:31ZengBMCBMC Bioinformatics1471-21052020-01-0121111410.1186/s12859-019-3316-1A pipeline to create predictive functional networks: application to the tumor progression of hepatocellular carcinomaMaxime Folschette0Vincent Legagneux1Arnaud Poret2Lokmane Chebouba3Carito Guziolowski4Nathalie Théret5Univ Rennes, Inria, CNRS, IRISA, UMR 6074Univ Rennes, Inserm, EHESP, Irset, UMR S1085LS2N, Laboratoire des Sciences du Numérique de Nantes, UMR 6004LS2N, Laboratoire des Sciences du Numérique de Nantes, UMR 6004LS2N, Laboratoire des Sciences du Numérique de Nantes, UMR 6004Univ Rennes, Inria, CNRS, IRISA, UMR 6074Abstract 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.https://doi.org/10.1186/s12859-019-3316-1Data and network integrationDiscrete modelingHepatocellular carcinomaSignaling and regulatory knowledgeKEGG |
| spellingShingle | Maxime Folschette Vincent Legagneux Arnaud Poret Lokmane Chebouba Carito Guziolowski Nathalie Théret A pipeline to create predictive functional networks: application to the tumor progression of hepatocellular carcinoma BMC Bioinformatics Data and network integration Discrete modeling Hepatocellular carcinoma Signaling and regulatory knowledge KEGG |
| 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 | Data and network integration Discrete modeling Hepatocellular carcinoma Signaling and regulatory knowledge KEGG |
| url | https://doi.org/10.1186/s12859-019-3316-1 |
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