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

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Material Information

Title:
Computational inference and analysis of genetic regulatory networks via a supervised combinatorial-optimization pattern
Physical Description:
Mixed Material
Language:
English
Creator:
Tang, Binhua
Wu, Xuechen
Tan, Ge
Chen, Su-Shing
Jing, Qing
Shen, Bairong
Publisher:
Bio-Med Central (BMC Systems Biology)
Publication Date:

Notes

Abstract:
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.
General Note:
Tang et al. BMC Systems Biology 2010, 4(Suppl 2):S3 http://www.biomedcentral.com/1752-0509/4?issue=S2/S3; Pages 1-13
General Note:
doi:10.1186/1752-0509-4-S2-S3 Cite this article as: Tang et al.: Computational inference and analysis of genetic regulatory networks via a supervised combinatorialoptimization pattern. BMC Systems Biology 2010 4(Suppl 2):S3.

Record Information

Source Institution:
University of Florida
Holding Location:
University of Florida
Rights Management:
All rights reserved by the source institution.
Resource Identifier:
oclc -
System ID:
AA00019210:00001

  • STANDARD VIEW
  • MARC VIEW

Material Information

Title:
Computational inference and analysis of genetic regulatory networks via a supervised combinatorial-optimization pattern
Physical Description:
Mixed Material
Language:
English
Creator:
Tang, Binhua
Wu, Xuechen
Tan, Ge
Chen, Su-Shing
Jing, Qing
Shen, Bairong
Publisher:
Bio-Med Central (BMC Systems Biology)
Publication Date:

Notes

Abstract:
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.
General Note:
Tang et al. BMC Systems Biology 2010, 4(Suppl 2):S3 http://www.biomedcentral.com/1752-0509/4?issue=S2/S3; Pages 1-13
General Note:
doi:10.1186/1752-0509-4-S2-S3 Cite this article as: Tang et al.: Computational inference and analysis of genetic regulatory networks via a supervised combinatorialoptimization pattern. BMC Systems Biology 2010 4(Suppl 2):S3.

Record Information

Source Institution:
University of Florida
Holding Location:
University of Florida
Rights Management:
All rights reserved by the source institution.
Resource Identifier:
oclc -
System ID:
AA00019210:00001