Earticle

초록

영어

Much effect has been devoted over the past decade to inference of gene regulatory networks (GRNs). However, the previous methods infer GRNs containing large amount of false positive edges, which could result in awful influence on biological analysis. In this study, we present a novel hybrid framework to improve the accuracy of GRN inference. In our method, network topologies from linear and nonlinear ordinary differential equation (ODE) models are integrated. The additive tree models are proposed for identification of linear/nonlinear models. We also propose a new criterion function that sparse and relevant terms are considered while inferring linear and nonlinear models. Benchmark datasets from Dialogue for Reverse Engineering Assessments and Methods challenge and real biological dataset from SOS DNA repair network in Escherichia coli are used to test the validity of our method. Results reveal that our proposed method can improve the prediction accuracy of GRN inference effectively and performs better than other popular methods.

목차

Abstract
 1. Introduction
 2. Method
  2.1. Mathematic model of transcriptional procedure
  2.2. Representation of additive tree model
  2.3. Structure optimization of models
  2.4. MI
  2.5. Evaluation of model using Particle Swarm Optimization
  2.6. Procedure of inferring gene regulatory network
 3. Experimental Results and Analysis
  3.1. Simulated data
  3.2. Real gene expression data
 4. Discussion
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Bin Yang [ School of Information Science and Engineering, Shandong University ]
• Mingyan Jiang [ School of Information Science and Engineering, Shandong University ]
• Yuehui Chen [ Computational Intelligence Lab, School of Information Science and Engineering, University of Jinan ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605