Robust background normalization method for one-channel microarrays

Purutçuoğlu Gazi, Vilda
Weber, Gerhard-Wilhelm
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.


Comparison of two inference approaches in Gaussian graphical models
Purutçuoğlu Gazi, Vilda; Wit, Ernst (Walter de Gruyter GmbH, 2017-04-01)
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.
Adaptive neuro-fuzzy inference system (ANFIS): A new approach to predictive modeling in QSAR applications: A study of neuro-fuzzy modeling of PCP-based NMDA receptor antagonists
Buyukbingol, Erdem; Sisman, Arzu; Akyıldız, Murat; Alpaslan, Ferda Nur; Adejare, Adeboye (Elsevier BV, 2007-06-15)
This paper proposes a new method, Adaptive Neuro-Fuzzy Inference System (ANFIS) to evaluate physicochemical descriptors of certain chemical compounds for their appropriate biological activities in terms of QSAR models with the aid of artificial neural network (ANN) approach combined with the principle of fuzzy logic. The ANFIS was utilized to predict NMDA (N-methyl-D-Aspartate) receptor binding activities of phencyclidine (PCP) derivatives. A data set of 38 drug-like compounds was coded with 1244 calculated...
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 parylene-based dual channel micro-electrophoresis system for rapid mutation detection via heteroduplex analysis
SUKAS, Sertan; Erson Bensan, Ayşe Elif; Sert, Cüneyt; Külah, Haluk (Wiley, 2008-09-01)
A new dual channel micro-electrophoresis system for rapid mutation detection based on heteroduplex analysis was designed and implemented. Mutation detection was successfully achieved in a total separation length of 250 pm in less than 3 min for a 590 by DNA sample harboring a 3 bp mutation causing an amino acid change. Parylene-C was used as the structural material for fabricating the micro-channels as it provides conformal deposition, transparency, biocompatibility, and low background fluorescence without ...
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 ...
Citation Formats
T. AKAL, V. Purutçuoğlu Gazi, and G.-W. Weber, “Robust background normalization method for one-channel microarrays,” TURKISH JOURNAL OF BIOCHEMISTRY-TURK BIYOKIMYA DERGISI, pp. 111–121, 2017, Accessed: 00, 2020. [Online]. Available: