Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 197 for “"Microarray data"”.
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Differential modeling for cancer microarray data
… for differential analysis of gene expression data.</p> <p>Secondly, we propose a novel differential network analysis approach that is composed of two algorithms, namely, DiffRank and DiffSubNet, to identify differential hubs and differential subnetworks, respectively. In this approach, two …
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Probe Level Analysis of Affymetrix Microarray Data
The analysis of Affymetrix GeneChip® data is a complex, multistep process. Most often, methodscondense the multiple probe level intensities into single probeset level measures (such as RobustMulti-chip Average (RMA), dChip and Microarray Suite version 5.0 (MAS5)), which are thenfollowed by …
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Bioinformatics framework for genotyping microarray data analysis
… in high-throughput instrumentation and microarray data analysis. Genotyping microarrays establish the genotypes of DNA sequences containing single nucleotide polymorphisms (SNPs), and can help biologists probe the functions of different genes and/or construct complex gene interaction …
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Unsupervised gene regulatory network inference on microarray data
… gene regulatory networks (GRNs) from expression data is a challenging and crucial task. Many computational methods and algorithms have been developed to infer gene networks for gene expression data, which are usually obtained from microarray experiments. A gene network is a method to depict the …
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Contributions to Statistical Problems Related to Microarray Data
Microarray is a high throughput technology to measure the gene expression. Analysis of microarray data brings many interesting and challenging problems. This thesis consists three studies related to microarray data. First, we propose a Bayesian model for microarray data and use Bayes Factors to …
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Probabilistic Finite Mixture Clustering of Genetic Expression Microarray Data
Exploratory cluster analysis of large data sets often implements k-means or hierarchical methods. These routines typically exhibit limitations which reduces the reliability of the results. Both assign observations to the one component for which its Euclidean distance from the center is smallest. …
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Optimizing parameters in fuzzy k-means for clustering microarray data.
Rapid advances of microarray technologies are making it possible to analyze and manipulate large amounts of gene expression data. Clustering algorithms, such as hierarchical clustering, self-organizing maps, k-means clustering and fuzzy k-means clustering, have become important tools for expression …
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New approaches to open problems in gene expression microarray data
… Among the most important innovations is the microarray tecnology. It allows to quantify the expression for thousands of genes simultaneously by measurin the hybridization from a tissue of interest to probes on a small glass or plastic slide. The characteristics of these data include a fair …
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Stem cell differentiation study: Statistical analysis and application to microarray data
… main aims: (1) To develop new statistical and data mining procedures for modeling stem cell genes during the differentiation process, (2) To present biological interpretations of "sternness" genes based on time-course expression patterns and genetic networks. Stem cells have been of much …
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Analyzing the dependence structure of microarray data: a copula–based approach
… is the study of clustering dependent data by means of copula functions with particular emphasis on microarray data. Copula functions are a popular multivariate modeling tool in each field where the multivariate dependence is of great interest and their use in clustering has not been …
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Contribution to Statistical Techniques for Identifying Differentially Expressed Genes in Microarray Data
With the development of DNA microarray technology, scientists can now measure the expression levels of thousands of genes (features or genomic biomarkers) simultaneously in one single experiment. Robust and accurate gene selection methods are required to identify differentially expressed genes …
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Detecting Phenotype-Specific Interactions Between Biological Processes From Microarray Data And Annotations
… of high throughput technologies such as DNA microarrays has enabled researchers to measure expression levels on a genomic scale. Correct and efficient biological interpretation of the voluminous data generated by these technologies, however, remains a challenging problem. A commonly used …
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Normal Mixture Models for Gene Cluster Identification in Two Dimensional Microarray Data
… dissertation focuses on methodology specific to microarray data analyses that organize the data in preliminary steps and proposes a cluster analysis method which improves the interpretability of the cluster results. Cluster analysis of microarray data allows samples with similar gene expression …
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DNA Microarray Data Analysis and Mining: Affymetrix Software Package and In-House Complementary Packages
Data management and analysis represent a major challenge for microarray studies. In this study, Affymetrix software was used to analyze an HIV-infection data. The microarray analysis shows remarkably different results when using different parameters provided by the software. This highlights the …
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EMMA2 : a MAGE-compliant system for the analysis of microarray data in integrated functional genomics
… to study gene-expression and metabolic pathways. Microarrays have become a highly popular method to measure the transcriptional regulation in functional genomics. Microarrays allow to measure the expression levels of thousands of genes in parallel, but the measured datasets contain a certain level …
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Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data
… are ensemble of trees methods widely used for data prediction, interpretation and variable selection purposes. The wide acceptance can be attributed to its robustness to high dimensionality problem. However, when the high-dimensional data is a sparse one, RF procedures are inefficient. Thus, …
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Microarray data analysis methods and their applications to gene expression data analysis for Saccharomyces cerevisiae under oxidative stress
… and pathway analysis were used to analyze the data. The results from this study provide a dynamic resolution of the oxidative stress responses in S. cerevisiae, and contribute to a richer understanding of the antioxidant defense systems. It also provides a global view of the roles that Yap1 …
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