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Global gene network exploration based on explainable artificial intelligence approach

In recent years, personalized gene regulatory networks have received significant attention, and interpretation of the multilayer networks has been a critical issue for a comprehensive understanding of gene regulatory systems. Although several statistical and machine learning approaches have been dev...

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Autores principales: Park, Heewon, Maruhashi, Koji, Yamaguchi, Rui, Imoto, Seiya, Miyano, Satoru
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7647077/
https://www.ncbi.nlm.nih.gov/pubmed/33156825
http://dx.doi.org/10.1371/journal.pone.0241508
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author Park, Heewon
Maruhashi, Koji
Yamaguchi, Rui
Imoto, Seiya
Miyano, Satoru
author_facet Park, Heewon
Maruhashi, Koji
Yamaguchi, Rui
Imoto, Seiya
Miyano, Satoru
author_sort Park, Heewon
collection PubMed
description In recent years, personalized gene regulatory networks have received significant attention, and interpretation of the multilayer networks has been a critical issue for a comprehensive understanding of gene regulatory systems. Although several statistical and machine learning approaches have been developed and applied to reveal sample-specific regulatory pathways, integrative understanding of the massive multilayer networks remains a challenge. To resolve this problem, we propose a novel artificial intelligence (AI) strategy for comprehensive gene regulatory network analysis. In our strategy, personalized gene networks corresponding specific clinical characteristic are constructed and the constructed network is considered as a second-order tensor. Then, an explainable AI method based on deep learning is applied to decompose the multilayer networks, thus we can reveal all-encompassing gene regulatory systems characterized by clinical features of patients. To evaluate the proposed methodology, we apply our method to the multilayer gene networks under varying conditions of an epithelial–mesenchymal transition (EMT) process. From the comprehensive analysis of multilayer networks, we identified novel markers, and the biological mechanisms of the identified genes and their reciprocal mechanisms are verified through the literature. Although any biological knowledge about the identified genes was not incorporated in our analysis, our data-driven approach based on AI approach provides biologically reliable results. Furthermore, the results provide crucial evidences to reveal biological mechanism related to various diseases, e.g., keratinocyte proliferation. The use of explainable AI method based on the tensor decomposition enables us to reveal global and novel mechanisms of gene regulatory system from the massive multiple networks, which cannot be demonstrated by existing methods. We expect that the proposed method provides a new insight into network biology and it will be a useful tool to integrative gene network analysis related complex architectures of diseases.
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spelling pubmed-76470772020-11-16 Global gene network exploration based on explainable artificial intelligence approach Park, Heewon Maruhashi, Koji Yamaguchi, Rui Imoto, Seiya Miyano, Satoru PLoS One Research Article In recent years, personalized gene regulatory networks have received significant attention, and interpretation of the multilayer networks has been a critical issue for a comprehensive understanding of gene regulatory systems. Although several statistical and machine learning approaches have been developed and applied to reveal sample-specific regulatory pathways, integrative understanding of the massive multilayer networks remains a challenge. To resolve this problem, we propose a novel artificial intelligence (AI) strategy for comprehensive gene regulatory network analysis. In our strategy, personalized gene networks corresponding specific clinical characteristic are constructed and the constructed network is considered as a second-order tensor. Then, an explainable AI method based on deep learning is applied to decompose the multilayer networks, thus we can reveal all-encompassing gene regulatory systems characterized by clinical features of patients. To evaluate the proposed methodology, we apply our method to the multilayer gene networks under varying conditions of an epithelial–mesenchymal transition (EMT) process. From the comprehensive analysis of multilayer networks, we identified novel markers, and the biological mechanisms of the identified genes and their reciprocal mechanisms are verified through the literature. Although any biological knowledge about the identified genes was not incorporated in our analysis, our data-driven approach based on AI approach provides biologically reliable results. Furthermore, the results provide crucial evidences to reveal biological mechanism related to various diseases, e.g., keratinocyte proliferation. The use of explainable AI method based on the tensor decomposition enables us to reveal global and novel mechanisms of gene regulatory system from the massive multiple networks, which cannot be demonstrated by existing methods. We expect that the proposed method provides a new insight into network biology and it will be a useful tool to integrative gene network analysis related complex architectures of diseases. Public Library of Science 2020-11-06 /pmc/articles/PMC7647077/ /pubmed/33156825 http://dx.doi.org/10.1371/journal.pone.0241508 Text en © 2020 Park et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Park, Heewon
Maruhashi, Koji
Yamaguchi, Rui
Imoto, Seiya
Miyano, Satoru
Global gene network exploration based on explainable artificial intelligence approach
title Global gene network exploration based on explainable artificial intelligence approach
title_full Global gene network exploration based on explainable artificial intelligence approach
title_fullStr Global gene network exploration based on explainable artificial intelligence approach
title_full_unstemmed Global gene network exploration based on explainable artificial intelligence approach
title_short Global gene network exploration based on explainable artificial intelligence approach
title_sort global gene network exploration based on explainable artificial intelligence approach
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7647077/
https://www.ncbi.nlm.nih.gov/pubmed/33156825
http://dx.doi.org/10.1371/journal.pone.0241508
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