Highdimensional Data
Mostrando 13-19 de 19 artigos, teses e dissertações.
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13. Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data
We describe a method for recovering the underlying parametrization of scattered data (mi) lying on a manifold M embedded in high-dimensional Euclidean space. The method, Hessian-based locally linear embedding, derives from a conceptual framework of local isometry in which the manifold M, viewed as a Riemannian submanifold of the ambient Euclidean space ℝn,
The National Academy of Sciences.
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14. Diagnosis of Ovarian Cancer Using Decision Tree Classification of Mass Spectral Data
Recent reports from our laboratory and others support the SELDI ProteinChip technology as a potential clinical diagnostic tool when combined with n-dimensional analyses algorithms. The objective of this study was to determine if the commercially available classification algorithm biomarker patterns software (BPS), which is based on a classification and regr
Hindawi Publishing Corporation.
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15. A self-organizing principle for learning nonlinear manifolds
Modern science confronts us with massive amounts of data: expression profiles of thousands of human genes, multimedia documents, subjective judgments on consumer products or political candidates, trade indices, global climate patterns, etc. These data are often highly structured, but that structure is hidden in a complex set of relationships or high-dimensio
National Academy of Sciences.
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16. Multi-class cancer classification by total principal component regression (TPCR) using microarray gene expression data
DNA microarray technology provides a promising approach to the diagnosis and prognosis of tumors on a genome-wide scale by monitoring the expression levels of thousands of genes simultaneously. One problem arising from the use of microarray data is the difficulty to analyze the high-dimensional gene expression data, typically with thousands of variables (gen
Oxford University Press.
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17. Network component analysis: Reconstruction of regulatory signals in biological systems
High-dimensional data sets generated by high-throughput technologies, such as DNA microarray, are often the outputs of complex networked systems driven by hidden regulatory signals. Traditional statistical methods for computing low-dimensional or hidden representations of these data sets, such as principal component analysis and independent component analysi
National Academy of Sciences.
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18. Local Context Finder (LCF) reveals multidimensional relationships among mRNA expression profiles of Arabidopsis responding to pathogen infection
A major task in computational analysis of mRNA expression profiles is definition of relationships among profiles on the basis of similarities among them. This is generally achieved by pattern recognition in the distribution of data points representing each profile in a high-dimensional space. Some drawbacks of commonly used pattern recognition algorithms ste
National Academy of Sciences.
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19. The E47 transcription factor negatively regulates CD5 expression during thymocyte development
The expression of CD5 increases progressively as thymocytes mature. We have shown that CD5 expression is controlled by a tissue-specific regulatory promoter located upstream of the CD5 translation start sites. Deletion of this regulatory promoter, which contains three potential transcription factor binding sites (CCAAT, κE2, and ets) reduces the promoter ac
National Academy of Sciences.