Earticle

초록

영어

Gene regulatory networks depict the interactions among genes in the cell and construction of networks is important in uncovering the underlying biological process of living organisms. In this paper, a non-linear differential equation model is used for gene regulatory network reconstruction and time-series prediction. A new model, called additive expression tree (AET) model is proposed to encode ordinary differential equations (ODEs). A new structure-based evolutionary algorithm and artificial bee colony (ABC) are used to optimize the architecture and parameters of the additive expression tree model, respectively. A synthetic data and two real time-series expression datasets are used to test the validity of our proposed model and hybrid approach. Experimental results demonstrate that our model could improve accuracy of microarray time-series data effectively.

목차

Abstract
 1. Introduction
 2. Representation of Additive Expression Tree Model
 3. The Proposed Hybrid Method
  3.1. Structure Optimization Methods
  3.2. Fitness Definition
  3.3. Parameter Optimization of Models
  3.4. Summary of Our Proposed Algorithm
 4. Experimental Results and Analysis
  4.1. Experiment with Biochemical Pathway
  4.2. Experiment with the Human Cell Time-Series Data
  4.3. Experiment with the E. Coli Database
 5. Conclusion and Discussion
 Acknowledgements
 References

키워드

저자

• Bin Yang [ School of Information Science and Engineering, Zaozhuang University, Zaozhuang, P.R. China 277160 ]

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