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Selective Sampling (selective + sampling)
Selected AbstractsSelective sampling for approximate clustering of very large data setsINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 3 2008Liang Wang A key challenge in pattern recognition is how to scale the computational efficiency of clustering algorithms on large data sets. The extension of non-Euclidean relational fuzzy c-means (NERF) clustering to very large (VL = unloadable) relational data is called the extended NERF (eNERF) clustering algorithm, which comprises four phases: (i) finding distinguished features that monitor progressive sampling; (ii) progressively sampling from a N × N relational matrix RN to obtain a n × n sample matrix Rn; (iii) clustering Rn with literal NERF; and (iv) extending the clusters in Rn to the remainder of the relational data. Previously published examples on several fairly small data sets suggest that eNERF is feasible for truly large data sets. However, it seems that phases (i) and (ii), i.e., finding Rn, are not very practical because the sample size n often turns out to be roughly 50% of n, and this over-sampling defeats the whole purpose of eNERF. In this paper, we examine the performance of the sampling scheme of eNERF with respect to different parameters. We propose a modified sampling scheme for use with eNERF that combines simple random sampling with (parts of) the sampling procedures used by eNERF and a related algorithm sVAT (scalable visual assessment of clustering tendency). We demonstrate that our modified sampling scheme can eliminate over-sampling of the original progressive sampling scheme, thus enabling the processing of truly VL data. Numerical experiments on a distance matrix of a set of 3,000,000 vectors drawn from a mixture of 5 bivariate normal distributions demonstrate the feasibility and effectiveness of the proposed sampling method. We also find that actually running eNERF on a data set of this size is very costly in terms of computation time. Thus, our results demonstrate that further modification of eNERF, especially the extension stage, will be needed before it is truly practical for VL data. © 2008 Wiley Periodicals, Inc. [source] Power and robustness of linkage tests for quantitative traits in general pedigreesGENETIC EPIDEMIOLOGY, Issue 1 2005Wei-Min Chen Abstract There are numerous statistical methods for quantitative trait linkage analysis in human studies. An ideal such method would have high power to detect genetic loci contributing to the trait, would be robust to non-normality in the phenotype distribution, would be appropriate for general pedigrees, would allow the incorporation of environmental covariates, and would be appropriate in the presence of selective sampling. We recently described a general framework for quantitative trait linkage analysis, based on generalized estimating equations, for which many current methods are special cases. This procedure is appropriate for general pedigrees and easily accommodates environmental covariates. In this report, we use computer simulations to investigate the power and robustness of a variety of linkage test statistics built upon our general framework. We also propose two novel test statistics that take account of higher moments of the phenotype distribution, in order to accommodate non-normality. These new linkage tests are shown to have high power and to be robust to non-normality. While we have not yet examined the performance of our procedures in the context of selective sampling via computer simulations, the proposed tests satisfy all of the other qualities of an ideal quantitative trait linkage analysis method. Genet. Epidemiol. © 2004 Wiley-Liss, Inc. [source] Simultaneous assessment of DNA ploidy and biomarker expression in paraffin-embedded tissue sectionsHISTOPATHOLOGY, Issue 1 2010Stijn J H M Fleskens Fleskens S J H M, Takes R P, Otte-Höller I, van Doesburg L, Smeets A, Speel E-J M, Slootweg P J & van der Laak J A W M (2010) Histopathology,57, 14,26 Simultaneous assessment of DNA ploidy and biomarker expression in paraffin-embedded tissue sections Aims:, Aneuploidy is a potential biomarker for predicting progression of premalignancies. Ploidy assessment is mostly performed on nuclei isolated from tissue sections. Ploidy assessment in situ in tissue sections may be a large improvement, enabling selective sampling of nuclei, thus allowing the correlation between ploidy and histology. Existing ploidy analysis methods in sections suffer from limited sensitivity. The aim was to reliably assess ploidy in sections, combined with simultaneous assessment of other markers at the individual cell level. Methods and results:, Ploidy was measured in 22 paraffin-embedded oral premalignancies. The DNA stoichiometric Feulgen procedure was used on isolated nuclei, as well as fluoresence in situ hybridization analysis. In tissue sections, Feulgen was combined with immunohistochemistry for Ki67 proliferation marker, enabling distinction between cycling euploid and aneuploid cells. Aneuploidy was reliably detected in tissue sections (sensitivity 100%, specificity 92%). One section in which aneuploidy was detected was misclassified in isolated nuclei analysis. Sections were also successfully analysed using our model combined with DNA double strand break marker ,-H2AX in fluorescence microscopy, underlining the power of biomarker evaluation on single cells in tissue sections. Conclusions:, The analysis model proposed in this study enables the combined analysis of histology, genotypic and phenotypic information. [source] Sentinel node biopsy in melanoma: Technical considerations of the procedure as performed at the john wayne cancer instituteJOURNAL OF SURGICAL ONCOLOGY, Issue 8 2010Sanjay P. Bagaria MD Abstract Since its first description in 1990, sentinel node (SN) biopsy has become the standard for accurate staging of a melanoma-draining regional lymphatic basin. This minimally invasive, multidisciplinary technique can detect occult metastases by selective sampling and focused pathologic analysis of the first nodes on the afferent lymphatic pathway from a primary cutaneous melanoma. An understanding of preoperative lymphoscintigraphy, intraoperative lymphatic mapping, and the definition of SN are critical for surgical expertise with SN biopsy. J. Surg. Oncol. 2010; 101:669-676. © 2010 Wiley-Liss, Inc. [source] | ||||||||||