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MICFuzzy: A maximal information content based fuzzy approach for reconstructing genetic networks
In systems biology, the accurate reconstruction of Gene Regulatory Networks (GRNs) is crucial since these networks can facilitate the solving of complex biological problems. Amongst the plethora of methods available for GRN reconstruction, information theory and fuzzy concepts-based methods have abi...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10328247/ https://www.ncbi.nlm.nih.gov/pubmed/37418430 http://dx.doi.org/10.1371/journal.pone.0288174 |
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author | Nakulugamuwa Gamage, Hasini Chetty, Madhu Lim, Suryani Hallinan, Jennifer |
author_facet | Nakulugamuwa Gamage, Hasini Chetty, Madhu Lim, Suryani Hallinan, Jennifer |
author_sort | Nakulugamuwa Gamage, Hasini |
collection | PubMed |
description | In systems biology, the accurate reconstruction of Gene Regulatory Networks (GRNs) is crucial since these networks can facilitate the solving of complex biological problems. Amongst the plethora of methods available for GRN reconstruction, information theory and fuzzy concepts-based methods have abiding popularity. However, most of these methods are not only complex, incurring a high computational burden, but they may also produce a high number of false positives, leading to inaccurate inferred networks. In this paper, we propose a novel hybrid fuzzy GRN inference model called MICFuzzy which involves the aggregation of the effects of Maximal Information Coefficient (MIC). This model has an information theory-based pre-processing stage, the output of which is applied as an input to the novel fuzzy model. In this preprocessing stage, the MIC component filters relevant genes for each target gene to significantly reduce the computational burden of the fuzzy model when selecting the regulatory genes from these filtered gene lists. The novel fuzzy model uses the regulatory effect of the identified activator-repressor gene pairs to determine target gene expression levels. This approach facilitates accurate network inference by generating a high number of true regulatory interactions while significantly reducing false regulatory predictions. The performance of MICFuzzy was evaluated using DREAM3 and DREAM4 challenge data, and the SOS real gene expression dataset. MICFuzzy outperformed the other state-of-the-art methods in terms of F-score, Matthews Correlation Coefficient, Structural Accuracy, and SS_mean, and outperformed most of them in terms of efficiency. MICFuzzy also had improved efficiency compared with the classical fuzzy model since the design of MICFuzzy leads to a reduction in combinatorial computation. |
format | Online Article Text |
id | pubmed-10328247 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-103282472023-07-08 MICFuzzy: A maximal information content based fuzzy approach for reconstructing genetic networks Nakulugamuwa Gamage, Hasini Chetty, Madhu Lim, Suryani Hallinan, Jennifer PLoS One Research Article In systems biology, the accurate reconstruction of Gene Regulatory Networks (GRNs) is crucial since these networks can facilitate the solving of complex biological problems. Amongst the plethora of methods available for GRN reconstruction, information theory and fuzzy concepts-based methods have abiding popularity. However, most of these methods are not only complex, incurring a high computational burden, but they may also produce a high number of false positives, leading to inaccurate inferred networks. In this paper, we propose a novel hybrid fuzzy GRN inference model called MICFuzzy which involves the aggregation of the effects of Maximal Information Coefficient (MIC). This model has an information theory-based pre-processing stage, the output of which is applied as an input to the novel fuzzy model. In this preprocessing stage, the MIC component filters relevant genes for each target gene to significantly reduce the computational burden of the fuzzy model when selecting the regulatory genes from these filtered gene lists. The novel fuzzy model uses the regulatory effect of the identified activator-repressor gene pairs to determine target gene expression levels. This approach facilitates accurate network inference by generating a high number of true regulatory interactions while significantly reducing false regulatory predictions. The performance of MICFuzzy was evaluated using DREAM3 and DREAM4 challenge data, and the SOS real gene expression dataset. MICFuzzy outperformed the other state-of-the-art methods in terms of F-score, Matthews Correlation Coefficient, Structural Accuracy, and SS_mean, and outperformed most of them in terms of efficiency. MICFuzzy also had improved efficiency compared with the classical fuzzy model since the design of MICFuzzy leads to a reduction in combinatorial computation. Public Library of Science 2023-07-07 /pmc/articles/PMC10328247/ /pubmed/37418430 http://dx.doi.org/10.1371/journal.pone.0288174 Text en © 2023 Nakulugamuwa Gamage et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://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 Nakulugamuwa Gamage, Hasini Chetty, Madhu Lim, Suryani Hallinan, Jennifer MICFuzzy: A maximal information content based fuzzy approach for reconstructing genetic networks |
title | MICFuzzy: A maximal information content based fuzzy approach for reconstructing genetic networks |
title_full | MICFuzzy: A maximal information content based fuzzy approach for reconstructing genetic networks |
title_fullStr | MICFuzzy: A maximal information content based fuzzy approach for reconstructing genetic networks |
title_full_unstemmed | MICFuzzy: A maximal information content based fuzzy approach for reconstructing genetic networks |
title_short | MICFuzzy: A maximal information content based fuzzy approach for reconstructing genetic networks |
title_sort | micfuzzy: a maximal information content based fuzzy approach for reconstructing genetic networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10328247/ https://www.ncbi.nlm.nih.gov/pubmed/37418430 http://dx.doi.org/10.1371/journal.pone.0288174 |
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