A Boolean network inference from time-series gene expression data using a genetic algorithm
| العنوان: | A Boolean network inference from time-series gene expression data using a genetic algorithm |
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| المؤلفون: | Yung-Keun Kwon, Shohag Barman |
| المصدر: | Bioinformatics. 34:i927-i933 |
| بيانات النشر: | Oxford University Press (OUP), 2018. |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | 0301 basic medicine, Statistics and Probability, Multivariate statistics, Theoretical computer science, Computer science, Reliability (computer networking), Gene regulatory network, Gene Expression, Inference, Biochemistry, Set (abstract data type), 03 medical and health sciences, 0302 clinical medicine, Genetic algorithm, Gene Regulatory Networks, Molecular Biology, Regulator gene, Reproducibility of Results, Mutual information, Computer Science Applications, Computational Mathematics, 030104 developmental biology, Boolean network, Computational Theory and Mathematics, ComputingMethodologies_GENERAL, Algorithms, 030217 neurology & neurosurgery |
| الوصف: | Motivation Inferring a gene regulatory network from time-series gene expression data is a fundamental problem in systems biology, and many methods have been proposed. However, most of them were not efficient in inferring regulatory relations involved by a large number of genes because they limited the number of regulatory genes or computed an approximated reliability of multivariate relations. Therefore, an improved method is needed to efficiently search more generalized and scalable regulatory relations. Results In this study, we propose a genetic algorithm-based Boolean network inference (GABNI) method which can search an optimal Boolean regulatory function of a large number of regulatory genes. For an efficient search, it solves the problem in two stages. GABNI first exploits an existing method, a mutual information-based Boolean network inference (MIBNI), because it can quickly find an optimal solution in a small-scale inference problem. When MIBNI fails to find an optimal solution, a genetic algorithm (GA) is applied to search an optimal set of regulatory genes in a wider solution space. In particular, we modified a typical GA framework to efficiently reduce a search space. We compared GABNI with four well-known inference methods through extensive simulations on both the artificial and the real gene expression datasets. Our results demonstrated that GABNI significantly outperformed them in both structural and dynamics accuracies. Conclusion The proposed method is an efficient and scalable tool to infer a Boolean network from time-series gene expression data. Supplementary information Supplementary data are available at Bioinformatics online. |
| تدمد: | 1460-2059 1367-4803 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6977cce763fb7db6c85d5d7949e1cf6bTest https://doi.org/10.1093/bioinformatics/bty584Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....6977cce763fb7db6c85d5d7949e1cf6b |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 14602059 13674803 |
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