Comparison of two inference approaches in Gaussian graphical models

Introduction: The Gaussian Graphical Model (GGM) is one of the well-known probabilistic models which is based on the conditional independency of nodes in the biological system. Here, we compare the estimates of the GGM parameters by the graphical lasso (glasso) method and the threshold gradient descent (TGD) algorithm.


Robust background normalization method for one-channel microarrays
AKAL, TÜLAY; Purutçuoğlu Gazi, Vilda; Weber, Gerhard-Wilhelm (Walter de Gruyter GmbH, 2017-04-01)
Background: Microarray technology, aims to measure the amount of changes in transcripted messages for each gene by RNA via quantifying the colour intensity on the arrays. But due to the different experimental conditions, these measurements can include both systematic and random erroneous signals. For this reason, we present a novel gene expression index, called multi-RGX (Multiple-probe Robust Gene Expression Index) for one-channel microarrays.
Loop-based conic multivariate adaptive regression splines is a novel method for advanced construction of complex biological networks
Ayyıldız Demirci, Ezgi; Purutçuoğlu Gazi, Vilda; Weber, Gerhard Wilhelm (2018-11-01)
The Gaussian Graphical Model (GGM) and its Bayesian alternative, called, the Gaussian copula graphical model (GCGM) are two widely used approaches to construct the undirected networks of biological systems. They define the interactions between species by using the conditional dependencies of the multivariate normality assumption. However, when the system's dimension is high, the performance of the model becomes computationally demanding, and, particularly, the accuracy of GGM decreases when the observations...
Artificial-neural-network prediction of hexagonal lattice parameters for non-stoichiometric apatites
Kockan, Umit; Ozturk, Fahrettin; Evis, Zafer (2014-01-01)
In this study, hexagonal lattice parameters (a and c) and unit-cell volumes of non-stoichiometric apatites of M-10(TO4)(6)X-2 are predicted from their ionic radii with artificial neural networks. A multilayer-perceptron network is used for training. The results indicate that the Bayesian regularization method with four neurons in the hidden layer with a tansig activation function and one neuron in the output layer with a purelin function gives the best results. It is found that the errors for the predicted ...
Using artificially generated spectral data to improve protein secondary structure prediction from Fourier transform infrared spectra of proteins
Severcan, M; Haris, PI; Severcan, Feride (Elsevier BV, 2004-09-15)
Secondary structures of proteins have been predicted using neural networks from their Fourier transform infrared spectra. To improve the generalization ability of the neural networks, the training data set has been artificially increased by linear interpolation. The leave-one-out approach has been used to demonstrate the applicability of the method. Bayesian regularization has been used to train the neural networks and the predictions have been further improved by the maximum-likelihood estimation method. T...
A Shrinkage Approach for Modeling Non-Stationary Relational Autocorrelation
Angın, Pelin (2008-12-19)
Recent research has shown that collective classification in relational data often exhibit significant performance gains over conventional approaches that classify instances individually. This is primarily due to the presence of autocorrelation in relational datasets, meaning that the class labels of related entities are correlated and inferences about one instance can be used to improve inferences about linked instances. Statistical relational learning techniques exploit relational autocorrelation by modeli...
Citation Formats
V. Purutçuoğlu Gazi and E. Wit, “Comparison of two inference approaches in Gaussian graphical models,” TURKISH JOURNAL OF BIOCHEMISTRY-TURK BIYOKIMYA DERGISI, pp. 203–211, 2017, Accessed: 00, 2020. [Online]. Available: