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As the magnitude of the experiment increases, it is common to combine various types of microarrays such as paired and non-paired microarrays from different laboratories or hospitals. Thus, it is important to analyze microarray data together to derive a combined conclusion after accounting for heterogeneity among data sets. One of the main objectives of the microarray experiment is to identify differentially...
Fusion of functional magnetic resonance imaging (fMRI) and genetic information is becoming increasingly important in biomarker discovery. These studies can contain vastly different types of information occupying different measurement spaces and in order to draw significant inferences and make meaningful predictions about genetic influence on brain activity; methodologies need to be developed that...
Computational methods for miRNA target prediction have already provided a large amount of potential targets, but many of them suffer from high false positive rates, therefore a post-processing filter is needed for the predicted targets. This paper proposed a statistical method based on first-order non-homogeneous Markov chain to explore the base-pairing complementarity characteristics between miRNAs...
Transcription factors (TFs) play an important role in regulating the expression of genes. The accurate measurement of transcription factor activities (TFAs) depends on a series of experimental technologies of molecular biology and is intractable in most practical situations. Some signal processing methods for blind source separation have been applied in the prediction of TFAs from gene expression...
Lung cancer has been the top leading cancer type for the past two decades and the overall survival rate for Non-Small Cell Lung Cancer (NSCLC) remains at a low rate of 15 percents. Current lung cancer prognosis using staging system has been studied and proven to be not accurate enough, especially on early stages. Therefore, a new prognostic model is desired. Using gene expression values of 442 Affymetrix...
An increasing number of studies have profiled gene expressions in tumor specimens using distinct microarray platforms and analysis techniques. With the accumulating amount of microarray data, one of the most challenging tasks is to develop robust statistical models to integrate their findings. We compare some recent methodologies on the field, with respect to ER status, and focus on a unified among...
Most of the proposed clustering approaches are heuristic in nature. As a result, it is difficult to interpret the obtained clustering outcomes from a statistical standpoint. Mixture model-based clustering has received much attention from the gene expression community due to its sound statistical background and its flexibility in data modeling. However, current clustering algorithms following the model-based...
To cluster genes in microarray gene expression data experimented over known classes (conditions), this paper provides a model-based gene cluster method. This method takes the normality for component densities from so called splitting and averaging of class data where the number of components is given, and designs component means by general linear model subject to various linear restrictions in order...
Microarray experiments have become the centerpiece of genetic and genomic studies. The great advantage of such experiments is that they allow simultaneous examination of expression levels of thousands of genes. Several articles have been written on design of microarray experiments considering different aspects such as efficiency and power. Many of these articles consider comparing a discrete set of...
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