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Computational inference and analysis of genetic regulatory networks via a supervised combinatorial-optimization pattern

BACKGROUND: Post-genome era brings about diverse categories of omics data. Inference and analysis of genetic regulatory networks act prominently in extracting inherent mechanisms, discovering and interpreting the related biological nature and living principles beneath mazy phenomena, and eventually...

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Detalles Bibliográficos
Autores principales: Tang, Binhua, Wu, Xuechen, Tan, Ge, Chen, Su-Shing, Jing, Qing, Shen, Bairong
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2982690/
https://www.ncbi.nlm.nih.gov/pubmed/20840730
http://dx.doi.org/10.1186/1752-0509-4-S2-S3
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author Tang, Binhua
Wu, Xuechen
Tan, Ge
Chen, Su-Shing
Jing, Qing
Shen, Bairong
author_facet Tang, Binhua
Wu, Xuechen
Tan, Ge
Chen, Su-Shing
Jing, Qing
Shen, Bairong
author_sort Tang, Binhua
collection PubMed
description BACKGROUND: Post-genome era brings about diverse categories of omics data. Inference and analysis of genetic regulatory networks act prominently in extracting inherent mechanisms, discovering and interpreting the related biological nature and living principles beneath mazy phenomena, and eventually promoting the well-beings of humankind. RESULTS: A supervised combinatorial-optimization pattern based on information and signal-processing theories is introduced into the inference and analysis of genetic regulatory networks. An associativity measure is proposed to define the regulatory strength/connectivity, and a phase-shift metric determines regulatory directions among components of the reconstructed networks. Thus, it solves the undirected regulatory problems arising from most of current linear/nonlinear relevance methods. In case of computational and topological redundancy, we constrain the classified group size of pair candidates within a multiobjective combinatorial optimization (MOCO) pattern. CONCLUSIONS: We testify the proposed approach on two real-world microarray datasets of different statistical characteristics. Thus, we reveal the inherent design mechanisms for genetic networks by quantitative means, facilitating further theoretic analysis and experimental design with diverse research purposes. Qualitative comparisons with other methods and certain related focuses needing further work are illustrated within the discussion section.
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spelling pubmed-29826902010-11-17 Computational inference and analysis of genetic regulatory networks via a supervised combinatorial-optimization pattern Tang, Binhua Wu, Xuechen Tan, Ge Chen, Su-Shing Jing, Qing Shen, Bairong BMC Syst Biol Proceedings BACKGROUND: Post-genome era brings about diverse categories of omics data. Inference and analysis of genetic regulatory networks act prominently in extracting inherent mechanisms, discovering and interpreting the related biological nature and living principles beneath mazy phenomena, and eventually promoting the well-beings of humankind. RESULTS: A supervised combinatorial-optimization pattern based on information and signal-processing theories is introduced into the inference and analysis of genetic regulatory networks. An associativity measure is proposed to define the regulatory strength/connectivity, and a phase-shift metric determines regulatory directions among components of the reconstructed networks. Thus, it solves the undirected regulatory problems arising from most of current linear/nonlinear relevance methods. In case of computational and topological redundancy, we constrain the classified group size of pair candidates within a multiobjective combinatorial optimization (MOCO) pattern. CONCLUSIONS: We testify the proposed approach on two real-world microarray datasets of different statistical characteristics. Thus, we reveal the inherent design mechanisms for genetic networks by quantitative means, facilitating further theoretic analysis and experimental design with diverse research purposes. Qualitative comparisons with other methods and certain related focuses needing further work are illustrated within the discussion section. BioMed Central 2010-09-13 /pmc/articles/PMC2982690/ /pubmed/20840730 http://dx.doi.org/10.1186/1752-0509-4-S2-S3 Text en Copyright ©2010 Shen et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Proceedings
Tang, Binhua
Wu, Xuechen
Tan, Ge
Chen, Su-Shing
Jing, Qing
Shen, Bairong
Computational inference and analysis of genetic regulatory networks via a supervised combinatorial-optimization pattern
title Computational inference and analysis of genetic regulatory networks via a supervised combinatorial-optimization pattern
title_full Computational inference and analysis of genetic regulatory networks via a supervised combinatorial-optimization pattern
title_fullStr Computational inference and analysis of genetic regulatory networks via a supervised combinatorial-optimization pattern
title_full_unstemmed Computational inference and analysis of genetic regulatory networks via a supervised combinatorial-optimization pattern
title_short Computational inference and analysis of genetic regulatory networks via a supervised combinatorial-optimization pattern
title_sort computational inference and analysis of genetic regulatory networks via a supervised combinatorial-optimization pattern
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2982690/
https://www.ncbi.nlm.nih.gov/pubmed/20840730
http://dx.doi.org/10.1186/1752-0509-4-S2-S3
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