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 19 of 19 for “"Biclustering"”.
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SNAP Biclustering
This thesis presents a new ant-optimized biclustering technique known as SNAP biclustering, which runs faster and produces results of superior quality to previous techniques. Biclustering techniques have been designed to compensate for the weaknesses of classical clustering algorithms by allowing …
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Enhanced Recommender Systems by Biclustering
… recommender systems, called BiRDS (meaning Biclustering Recommendation Systems) and develop three different modeling approaches under the new framework: iBiRDS (imputation BiRDS), rBiRDS (regularization BiRDS), lBiRDS (logistic BiRDS). All the three methods utilize biclustering to …
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A Biclustering Approach to Combinatorial Transcription Control
… fail to address this issue. We propose a novel biclustering algorithm based on random sampling to identify candidate binding site combinations. We establish bounds on the various parameters to the algorithm and study the conditions under which the algorithm is guaranteed to identify candidate …
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Biclustering and Visualization of High Dimensional Data using VIsual Statistical Data Analyzer
… these difficulties. One such technique, biclustering, clusters data in both dimensions and is inherently resistant to the challenges posed by having too many features. However, the algorithms that implement biclustering have limitations in that the user must know at least the structure of …
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Integrating biclustering techniques with de novo gene regulatory network discovery using RNA-seq from skeletal tissues
… using differential expression, clustering and biclustering algorithms, to detect similarly expressed genes, which provides evidence for genes potentially interacting together to produce a particular phenotype. Identifying key regulators in the gene regulatory networks (GRNs) driving cartilage …
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Clustering of immune-mediated diseases using genomic data
… statistics. I conducted an extensive study of biclustering methods and found that a Bayesian biclustering method called SSLB had best performance on simulated datasets and recovered biologically relevant biclusters in a knockout mouse dataset and a sorted blood cell dataset. Through the study I …
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Identification of Differentially Expressed Gene Modules in Heterogeneous Diseases
… heterogeneous diseases. It further focuses on biclustering methods which seem to be very promising in the context of disease heterogeneity. They are capable of identifying genes with a similar expression pattern in a previously unknown subset of samples. After an overview of existing …
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Desarrollo de técnicas de aprendizaje automático y computación evolutiva multiobjetivo para la inferencia de redes de asociación entre vías biológicas
… por datos de experimentos de microarray mediante biclustering. De esta forma, se busca proveer una metodología bioinformática que identifique relaciones entre rutas biológicas y las explique, proporcionando información útil para asistir a expertos en biología molecular. Para cumplir este objetivo …
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Bicluster-Based Identification of Gene Sets Through Multivariate Meta-Analysis (MVMA)
… in order to increase statistical strength? Biclustering has been proven to be highly effective for identifying gene sets. Compared to traditional clustering methods, biclustering recognizes a list of genes that are up- or down-regulated under a subset of the conditions, as opposed to the …
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Solving Intelligence Analysis Problems using Biclusters
… MineVis — an analytics system that integrates biclustering algorithms and visual analytics tools in one seamless environment. The combination of biclusters and visual data glyphs in a visual analytics spatial environment enables a novel type of filtering. This design allows for rapid …
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Reducibility and computational lower bounds for problems with planted sparse structure
… planted independent set, planted dense subgraph, biclustering, sparse rank-1 submatrix, sparse PCA and the subgraph stochastic block model. Our results demonstrate that, despite the delicate nature of average-case reductions, using natural problems as intermediates can often be beneficial, as is …
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Perfect Recovery in Heterogeneous Stochastic Bicluster Models
… which groups only objects based on similarity, biclustering simultaneously partitions both objects and features into subgroups, known as biclusters, based on their expression patterns. We model this as the densest k-disjoint-biclique problem, in which a weighted complete bipartite graph is …
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Insights on the Thermal Efficiency of SAGD from Data Analytics
… The second thesis study develops a new Bayesian biclustering method to find and differentiate groups within 328 SAGD well pairs based on their oil production response to steam injection over time. Clusters are described with probability distributions that capture the likelihood of transitioning …
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Reverse-Engineering of Genetic Regulatory Pathways in Human Cancer
… modules. Finally, a previously published biclustering approach, cMonkey, is adopted to identify molecular-based tumour subclasses (MetaChips) by searching for similarity in the expression of subsets of genes across subsets of tumours. Application of the method to breast cancer data shows …
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The Landscape of Host Transcriptional Response Programs Commonly Perturbed by Infectious Pathogens: Towards Host-Oriented Broad-Spectrum Drug
… methods combine gene set-level enrichment with biclustering. We applied our approach to a compendium of gene expression data sets derived from host cells exposed to bacterial or to fungal pathogens, to functional annotation data from multiple databases, and to drug targets from DrugBank. We …
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Biologically-Interpretable Disease Classification Based on Gene Expression Data
Classification of tissues and diseases based on gene expression data is a powerful application of DNA microarrays. Many popular classifiers like support vector machines, nearest-neighbour methods, and boosting have been applied successfully to this problem. However, it is difficult to determine …
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On the analysis of complex networks : fundamental limits, scalable algorithms, and applications
… clustering setups. In Chapter 8, we consider the biclustering problem, the analog of clustering on bipartite graphs. This problem has several applications such as inference of co-regulated genes, document classification, and so on. Here we propose an algorithm based on message-passing that closely …
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Differential modeling for cancer microarray data
<p>Capturing the changes between two biological phenotypes is a crucial task in understanding the mechanisms of various diseases. Most of the existing computational approaches depend on testing the changes in the expression levels of each single gene individually. In this work, we proposed novel …