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A network embedding approach to identify active modules in biological interaction networks
The identification of condition-specific gene sets from transcriptomic experiments is important to reveal regulatory and signaling mechanisms associated with a given cellular response. Statistical methods of differential expression analysis, designed to assess individual gene variations, have troubl...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Life Science Alliance LLC
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10282331/ https://www.ncbi.nlm.nih.gov/pubmed/37339804 http://dx.doi.org/10.26508/lsa.202201550 |
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author | Pasquier, Claude Guerlais, Vincent Pallez, Denis Rapetti-Mauss, Raphaël Soriani, Olivier |
author_facet | Pasquier, Claude Guerlais, Vincent Pallez, Denis Rapetti-Mauss, Raphaël Soriani, Olivier |
author_sort | Pasquier, Claude |
collection | PubMed |
description | The identification of condition-specific gene sets from transcriptomic experiments is important to reveal regulatory and signaling mechanisms associated with a given cellular response. Statistical methods of differential expression analysis, designed to assess individual gene variations, have trouble highlighting modules of small varying genes whose interaction is essential to characterize phenotypic changes. To identify these highly informative gene modules, several methods have been proposed in recent years, but they have many limitations that make them of little use to biologists. Here, we propose an efficient method for identifying these active modules that operates on a data embedding combining gene expressions and interaction data. Applications carried out on real datasets show that our method can identify new groups of genes of high interest corresponding to functions not revealed by traditional approaches. Software is available at https://github.com/claudepasquier/amine. |
format | Online Article Text |
id | pubmed-10282331 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Life Science Alliance LLC |
record_format | MEDLINE/PubMed |
spelling | pubmed-102823312023-06-22 A network embedding approach to identify active modules in biological interaction networks Pasquier, Claude Guerlais, Vincent Pallez, Denis Rapetti-Mauss, Raphaël Soriani, Olivier Life Sci Alliance Methods The identification of condition-specific gene sets from transcriptomic experiments is important to reveal regulatory and signaling mechanisms associated with a given cellular response. Statistical methods of differential expression analysis, designed to assess individual gene variations, have trouble highlighting modules of small varying genes whose interaction is essential to characterize phenotypic changes. To identify these highly informative gene modules, several methods have been proposed in recent years, but they have many limitations that make them of little use to biologists. Here, we propose an efficient method for identifying these active modules that operates on a data embedding combining gene expressions and interaction data. Applications carried out on real datasets show that our method can identify new groups of genes of high interest corresponding to functions not revealed by traditional approaches. Software is available at https://github.com/claudepasquier/amine. Life Science Alliance LLC 2023-06-20 /pmc/articles/PMC10282331/ /pubmed/37339804 http://dx.doi.org/10.26508/lsa.202201550 Text en © 2023 Pasquier et al. https://creativecommons.org/licenses/by/4.0/This article is available under a Creative Commons License (Attribution 4.0 International, as described at https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Methods Pasquier, Claude Guerlais, Vincent Pallez, Denis Rapetti-Mauss, Raphaël Soriani, Olivier A network embedding approach to identify active modules in biological interaction networks |
title | A network embedding approach to identify active modules in biological interaction networks |
title_full | A network embedding approach to identify active modules in biological interaction networks |
title_fullStr | A network embedding approach to identify active modules in biological interaction networks |
title_full_unstemmed | A network embedding approach to identify active modules in biological interaction networks |
title_short | A network embedding approach to identify active modules in biological interaction networks |
title_sort | network embedding approach to identify active modules in biological interaction networks |
topic | Methods |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10282331/ https://www.ncbi.nlm.nih.gov/pubmed/37339804 http://dx.doi.org/10.26508/lsa.202201550 |
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