MARS as an alternative approach of Gaussian graphical model for biochemical networks

AYYILDIZ DEMİRCİ E., Agraz M., PURUTÇUOĞLU GAZİ V.

JOURNAL OF APPLIED STATISTICS, cilt.44, sa.16, ss.2858-2876, 2017 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 44 Sayı: 16
  • Basım Tarihi: 2017
  • Doi Numarası: 10.1080/02664763.2016.1266465
  • Dergi Adı: JOURNAL OF APPLIED STATISTICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.2858-2876
  • Anahtar Kelimeler: Deterministic inference, multivariate adaptive regression splines, optimal model selection, Monte Carlo simulations, systems biology, REGULATORY NETWORKS, REGRESSION, OPTIMIZATION, SELECTION, INFERENCE, ROBUSTIFICATION, UNCERTAINTY, SHRINKAGE, DYNAMICS, FINANCE
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

The Gaussian graphical model (GGM) is one of the well-known modelling approaches to describe biological networks under the steady-state condition via the precision matrix of data. In literature there are different methods to infer model parameters based on GGM. The neighbourhood selection with the lasso regression and the graphical lasso method are the most common techniques among these alternative estimation methods. But they can be computationally demanding when the system's dimension increases. Here, we suggest a non-parametric statistical approach, called the multivariate adaptive regression splines (MARS) as an alternative of GGM. To compare the performance of both models, we evaluate the findings of normal and non-normal data via the specificity, precision, F-measures and their computational costs. From the outputs, we see that MARS performs well, resulting in, a plausible alternative approach with respect to GGM in the construction of complex biological systems.