Robust Gene Expression Index

The frequentist gene expression index (FGX) was recently developed to measure expression on Affymetrix oligonucleotide DNA arrays. In this study, we extend FGX to cover nonnormal log expressions, specifically long-tailed symmetric densities and call our new index as robust gene expression index (RGX). In estimation, we implement the modified maximum likelihood method to unravel the elusive solutions of likelihood equations and utilize the Fisher information matrix for covariance terms. From the analysis via the bench-mark datasets and simulated data, it is shown that RGX has promising results and mostly outperforms FGX in terms of relative efficiency of the estimated signals, in particular, when the data are nonnormal.


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.
Robust time series: Some engineering applications
Tiku, ML; Kestel, Sevtap Ayşe (2000-01-01)
AR(1) models in time series with nonnormal errors represented by two families of distributions: (i) Gamma with support IR:(0,infinity), and (ii) Student's t with support IR:(-infinity,infinity) are considered. Since the maximum likelihood (ML) estimators are intractable, the modified maximum likelihood (MML) estimators of the parameters are derived and it is shown that they are remarkably efficient besides being easy to compute. It is also shown that the least squares (LS) estimators have very low efficienc...
Prediction of protein subcellular localization using global protein sequence feature
Bozkurt, Burçin; Atalay, Mehmet Volkan; Department of Computer Engineering (2003)
The problem of identifying genes in eukaryotic genomic sequences by computational methods has attracted considerable research attention in recent years. Many early approaches to the problem focused on prediction of individual functional elements and compositional properties of coding and non coding deoxyribonucleic acid (DNA) in entire eukaryotic gene structures. More recently, a number of approaches has been developed which integrate multiple types of information including structure, function and genetic p...
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.
Robust multiobjective evolutionary feature subset selection algorithm for binary classification using machine learning techniques
Deniz, Ayca; Kiziloz, Hakan Ezgi; Dokeroglu, Tansel; Coşar, Ahmet (2017-06-07)
This study investigates the success of a multiobjective genetic algorithm (GA) combined with state-of-the-art machine learning (ML) techniques for the feature subset selection (FSS) in binary classification problem (BCP). Recent studies have focused on improving the accuracy of BCP by including all of the features, neglecting to determine the best performing subset of features. However, for some problems, the number of features may reach thousands, which will cause too much computation power to be consumed ...
Citation Formats
V. Purutçuoğlu Gazi, “Robust Gene Expression Index,” MATHEMATICAL PROBLEMS IN ENGINEERING, pp. 0–0, 2012, Accessed: 00, 2020. [Online]. Available: