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The Model Selection Methods for Sparse Biological Networks

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Artificial Intelligence and Applied Mathematics in Engineering Problems (ICAIAME 2019)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 43))

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Abstract

It is still crucial problem to estimate high dimensional graphical models and to choose the regularization parameter in dependent data. There are several classical methods such as Akaike’s information criterion and Bayesian Information criterion to solve this problem, but also more recent methods have been proposed such as stability selection and stability approach to regularization selection method (StARS) and some extensions of AIC and BIC which are more appropriate for high dimensional datasets. In this review, we give some overview about these methods and also give their consistency properties for graphical lasso. Then, we evaluate the performance of these approaches in real datasets. Finally, we propose the theoretical background of our proposal model selection criterion that is based on the KL-divergence and the bootstrapping computation, and is particularly suggested for the sparse biological networks.

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Acknowledgement

The authors thank to Ms Gül Bahar Bülbül for her help while preparing the tables.

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Authors and Affiliations

  1. Biomedical Engineering, Middle East Technical University, 06800, Ankara, Turkey

    Mehmet Ali Kaygusuz & Vilda Purutçuoğlu

  2. Department of Statistics, Middle East Technical University, 06800, Ankara, Turkey

    Vilda Purutçuoğlu

Authors
  1. Mehmet Ali Kaygusuz
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  2. Vilda Purutçuoğlu
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Corresponding author

Correspondence to Vilda Purutçuoğlu .

Editor information

Editors and Affiliations

  1. Department of ECE, Karunya University, Coimbatore, Tamil Nadu, India

    D. Jude Hemanth

  2. Department of Computer Engineering, Faculty of Engineering, Suleyman Demirel University, Isparta, Isparta, Turkey

    Utku Kose

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Kaygusuz, M.A., Purutçuoğlu, V. (2020). The Model Selection Methods for Sparse Biological Networks. In: Hemanth, D., Kose, U. (eds) Artificial Intelligence and Applied Mathematics in Engineering Problems. ICAIAME 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 43. Springer, Cham. https://doi.org/10.1007/978-3-030-36178-5_10

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