Comprehensive Analyses of Gaussian Graphical Model under Different Biological Networks

• Authors: D. Dokuzoğlu , V. Purutçuoğlu
• Languages of publication: EN

Abstracts

EN

Naturally, genes interact with each other by forming a complicated network and the relationship between groups of genes can be shown by different functions as gene networks. Recently, there has been a growing concern in uncovering these complex structures from gene expression data by modeling them mathematically. The Gaussian graphical model is one of the very popular parametric approaches for modelling the underlying types of biochemical systems. In this study, we evaluate the performance of this probabilistic model via different criteria, from the change in dimension of the systems to the change in the distribution of the data. Hereby, we generate high dimensional simulated datasets via copulas and apply them in Gaussian graphical model to compare sensitivity, specificity, F-measure and various other accuracy measures. We also assess its performance under real datasets. We consider that such comprehensive analyses can be helpful for assessing the limitation of this common model and for developing alternative approaches, to overcome its disadvantages.

Keywords

EN

02.50.-r; 02.50.Ng; 02.70.Rr

Discipline

• 02.50.Ng: Distribution theory and Monte Carlo studies
• 02.50.-r: Probability theory, stochastic processes, and statistics(see also section 05 Statistical physics, thermodynamics, and nonlinear dynamical systems)
• 02.70.Rr: General statistical methods

• Journal: Acta Physica Polonica A
• Year: 2017
• Volume: 132
• Issue: 3
• Pages: 1106-1111

Dates

published

2017-09

Contributors

author

D. Dokuzoğlu

• Middle East Technical University, Department of Statistics, Ankara, Turkey

author

V. Purutçuoğlu

• Middle East Technical University, Department of Statistics, Ankara, Turkey

References

• [1] Z. Akhmetova, S. Zhuzbaev, S. Boranbayev, Acta Phys. Pol. A 130, 352 (2016), doi: 10.12693/APhysPolA.130.352
• [2] B. Gürbüz, M. Sezer, Acta Phys. Pol. A 130, 194 (2016), doi: 10.12693/APhysPolA.130.194
• [3] E. Boutalbi, A. Gougam, F. Mekideche-Chafa, Acta Phys. Pol. A 128, B-271 (2015), doi: 10.12693/APhysPolA.128.B-271
• [4] K. Ergen, A. Çıllı, N. Yahnıoğlu, Acta Phys. Pol. A 128, B-273 (2015), doi: 10.12693/APhysPolA.128.B-273
• [5] M.J. Pazdanowski, Acta Phys. Pol. A 128, B-213 (2015), doi: 10.12693/APhysPolA.128.B-213
• [6] A. Recioui, Acta Phys. Pol. A 128, B-7 (2015), doi: 10.12693/APhysPolA.128.B-7
• [7] İ.S. Üncü, A. Arisoy, B. Büyükarikan, Acta Phys. Pol. A 128, B-474 (2015), doi: 10.12693/APhysPolA.128.B-474
• [8] J. Whittaker, Graphical models in Applied Multivariate Statistics, John Wiley and Sons, Chichester 1990
• [9] N. Meinshausen, P. Bühlmann, Ann. Stat. 34, 1436 (2006), doi: 10.1214/009053606000000281
• [10] J.H. Friedman, T. Hastie, R. Tibshirani, Biostatistics 9, 432 (2008), doi: 10.1093/biostatistics/kxm045
• [11] H. Liu, K. Roeder, L. Wasserman, in: Advances in Neural Information Processing Systems (NIPS), 2010, p. 1
• [12] J. Chen, Z. Chen, Biometrika 95, 759 (2008), doi: 10.1093/biomet/asn034
• [13] T. Zhao, H. Luin, N. Simon, J. Mach. Learn. Res. 13, 1059 (2012)
• [14] R.B. Nelsen, An Introduction to Copulas, 2nd ed., Springer Science and Business Media, Portland 2006
• [15] M.M. Wawrzyniak, D. Kurowicka, Dependence concepts, Delft University of Technology, Netherlands 2006
• [16] A.L. Barabási, Z.N. Oltvai, Nature Rev. Genetics 5, 101 (2004), doi: 10.1038/nrg1272
• [17] E. Limpert, W.A. Stahel, M. Abbt, BioScience 51, 341 (2001), doi: 10.3410/f.1020726.238430
• [18] K. Sachs, O. Perez, D. Pe'er, D. Lauffenburger, G. Nolan, Science 308, 523 (2005), doi: 10.1126/science.1105809
• [19] B. Stranger, A. Nica, M. Forrest, A. Dimas, C. Bird, C. Beazley, C. Ingle, M. Dunning, P. Flicek, S. Montgomery, S. Tavare, P. Deloukas, E. Dermitzakis, Nature Genetics 39, 1217 (2007), doi: 10.1038/ng2142
• [20] A. Bhadra, B.K. Mallick, Biometrics 69, 447 (2013), doi: 10.1111/biom.12021
• [21] L. Chen, F. Emmert-Streib, J. Storey, Genome Biol. 8, R219 (2007), doi: 10.1186/gb-2007-8-10-r219
• [22] T. Hastie, R. Tibshirani, J.H. Friedman, The Elements of Statistical Learning, Springer Verlag, New York 2001

Identifiers

DOI

10.12693/APhysPolA.132.1106