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 15 of 15 for “"Co-clustering"”.
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SPHERICAL AND STOCHASTIC CO-CLUSTERING ALGORITHMS
Clustering, without a doubt, is a dominating area in data mining and machine learning field. Due to the wide range of the necessity to clustering algorithms, it has many applications in real-life problems, ranging from bioinformatics to personalized information delivery. Feature characteristics of …
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NCIS: a network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression
… treatments. In this thesis, we introduce a new co-clustering algorithm for cancer subtype identification, which combines the information of gene networks to simultaneously group samples and genes into biologically meaningful clusters. We call our method network-assisted co-clustering for the …
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Data Clustering And Visualization Through Matrix Factorization
<p>Clustering is traditionally an unsupervised task which is to find natural groupings or clusters in multidimensional data based on perceived similarities among the patterns. The purpose of clustering is to extract useful information</p> <p>from unlabeled data.</p> <p>In order to present the …
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Differential modeling for cancer microarray data
… 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 computational approaches to identify the differential genes between two phenotypes. These approaches …
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Bayesian nonparametric learning for complicated text mining
… of application scenarios and a potentially high commercial value. It is commonly accepted that Bayesian models with finite-dimensional probability distributions as building blocks, also known as parametric topic models, are effective tools for text mining. However, one problem in existing …
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A Computational Framework for Learning from Complex Data: Formulations, Algorithms, and Applications
… are dynamically changing over time. As a consequence, the observed complex data generated by these processes also evolve smoothly. For example, in computational biology, the expression data matrices are evolving, since gene expression controls are deployed sequentially during development …
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Post-processing Techniques for Word Embedding
… that arise due to flaws in the training corpus. In this thesis, we propose a simple method to improve the quality of word embeddings and reveal their hidden structure using a post-processing technique. We deploy co-clustering techniques to detect sub-matrices between word meaning and …
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Learning on Inhomogeneous Hypergraphs
… to representing pairwise interactions. By contrast, in many real-world applications the entities engage in higher-order relations. Such relations can be modeled by hypergraphs, where the notion of an edge is generalized to a hyperedge that can connect more than two vertices. Traditional …
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B Cell Signaling and Bioinformatics: Revealing Components of the MHC Class II Antigen Processing and Presentation Pathway
… ligands induces phenotypic changes through complex signal transduction pathways. Gene expression is altered as a result of these changes and re-programs the cell to undergo differentiation, activation, effecter function, anergy, and/or apoptosis. Gene expression microarrays are used to …
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Computational methodologies and resources for discovery of phosphorylation regulation and function in cellular networks
… of signaling molecules. The rate of discovery of PTM sites is increasing rapidly and is significantly outpacing our biological understanding of the function and regulation of those modifications. The ten-fold increase in known phosphorylation sites over a five year time span can …
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DISRUPTION OF THE HIPPOCAMPAL GABAERGIC SYSTEM IN THE Fgf14 ̶ / ̶ MOUSE MODEL
In CNS, cognitive function is greatly dependent on the functional integrity of gamma-amino-butyric acid (GABA) interneurons. These inhibitory interneurons modulate synaptic plasticity and intrinsic excitability of principal neurons balancing the excitatory/inhibitory ratio in the brain. Reduction …
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Diluted Magnetic Semiconductor Cobalt and Europium Implanted ZnO Thin Film
Diluted magnetic semiconductors (DMSs) have initiated enormous scientific interests because of their potential for multifunctional spintronics devices. ZnO based semiconductors have been identified to be the promising room temperature ferromagnetic materials with a wide band-gap. However, the …
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Markov random field modeling of the spatial distribution of proteins on cell membranes
… in membrane molecular topography, including the co-clustering of receptors with signaling molecules and the segregation of other signaling molecules away from receptors. Electron microscopy of immunogold-labeled membranes is a critical technique to generate topographical information at the 5-10 …
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Mining Complex High-Order Datasets
… structure for storage and analysis of complex datasets is a vital but often overlooked decision in the design of data mining and machine learning experiments. Most present techniques impose a matrix structure on the dataset, with rows representing observations and columns representing …