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Novel model selection criteria on sparse biological networks

  • G. B. Bülbül
  • V. PurutçuoğluEmail author
  • E. Purutçuoğlu
Review
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Abstract

In statistical literature, gene networks are represented by graphical models, known by their sparsity in high dimensions. In this study, we suggest novel model selection criteria, namely, ICOMP, CAIC and CAICF to apply on simulated gene networks when selecting an optimal model among alternative estimated networks’ constructions. In this description, we build models with the Gaussian graphical model (GGM) and the inference of GGM is achieved via the graphical lasso method. In the assessment of our proposed model selection criteria, we compare their accuracies with other well-known criteria in this field under various dimensions and topologies of networks.

Keywords

Model selection criteria Simulated gene networks Gaussian graphical model 

Notes

Acknowledgements

The authors thank the METU Research Grant (No. BAP-08-11-2017-035) and the COSTNET Project (No. CA15109) for their support. Moreover, they thank anonymous referees and the editor for their valuable comments which improve the quality of the paper significantly.

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Copyright information

© Islamic Azad University (IAU) 2019

Authors and Affiliations

  • G. B. Bülbül
    • 1
  • V. Purutçuoğlu
    • 1
    Email author
  • E. Purutçuoğlu
    • 2
  1. 1.Department of StatisticsMiddle East Technical University (METU)AnkaraTurkey
  2. 2.Department of Social ServiceAnkara UniversityAnkaraTurkey

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