Results 201  250 of 1824  
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Tang DongMing   2010
Serial analysis of gene expression (SAGE) is a powerful tool to obtain gene expression profiles. Clustering analysis is a valuable technique for analyzing SAGE data. In this paper, we propose an adaptive clustering method for SAGE data analysis, namely, PoissonAPS. The method incorporates a novel clustering algorithm, Affinity Propagation (AP). ...


Xiong Hailing   2010
Colloid aggregation is often induced by the change of internal or external conditions. In order to account for the dynamic features of the evolutional open system, a conceptual model for colloid aggregation in open systems was developed based on the classic ClusterCluster Aggregation (CCA) model. The extended model allows the ...


Sun Zhibin   2010
Two types of artificial neural networks, multilayer perceptron (MLP) and selforganizing feature map (SOM) were used to detect mastitis by automatic milking systems (AMS) using a new mastitis indicator that combined two previously reported indicators based on higher electrical conductivity (EC) and lower quarter yield (QY). Four MLPs with four ...


Xie Benhuai   2010
Modelbased clustering has been widely used, e.g. in microarray data analysis. Since for highdimensional data variable selection is necessary, several penalized modelbased clustering methods have been proposed tørealize simultaneous variable selection and clustering. However, the existing methods all assume that the variables are independent with the use of diagonal covariance ...


AntonyBabu Sanjay   2010
Large numbers of alkaliphilic streptomycetes isolated from a beach and dune sand system were dereplicated manually based on aerial spore mass, colony reverse and diffusible pigment colours formed on oatmeal agar, and on their capacity to produce melanin pigments on peptoneyeast extractiron agar. The resultant data were converted to their ...


Arnonkijpanich Banchar   2010
Electronic data sets are increasing rapidly with respect to both, size of the data sets and data resolution, i.e. dimensionality, such that adequate data inspection and data visualization have become central issues of data mining. In this article, we present an extension of classical clustering schemes by local matrix adaptation, ...


Colombari Andrea   2010
In this paper, we propose a patchbased technique for robust background initialization that exploits both spatial and temporal consistency of the static background. The proposed technique is able to cope with heavy clutter, i.e, foreground objects that stand still for a considerable portion of time. First, the sequence is subdivided ...


Catlow C Richard A   2010
We review the growing role of computational techniques in modelling the structures and properties of nanoparticulate oxides and sulphides. We describe the main methods employed, including those based on both electronic structure and interatomic potential approaches. Particular attention is paid to the techniques used in searching for global minima in ...


Zeng Donglin D Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.   2010
We propose a class of additive transformation risk models for clustered failure time data. Our models are motivated by the usual additive risk model for independent failure times incorporating a frailty with mean one and constant variability which is a natural generalization of the additive risk model from univariate failure ...


Chwiej Joanna   2010
Xray fluorescence microscopy was applied for twodimensional elemental analysis of substantia nigra (SN) tissue. The samples representing Parkinson's disease (PD) and control cases were examined at HASYLAB beamline L and at ESRF beamline ID22. Twodimensional mapping of P, S, Cl, K, Ca, Fe, Cu, Zn, Se and Br was done ...


Bouguila Nizar   2010
In this paper, we propose a clustering algorithm based on both Dirichlet processes and generalized Dirichlet distribution which has been shown to be very flexible for proportional data modeling. Our approach can be viewed as an extension of the finite generalized Dirichlet mixture model to the infinite case. The extension ...


Mansell Jim   2009
BACKGROUND: The purpose of this review was to evaluate the available research on the quality and costs of dispersed communitybased housing when compared with clustered housing. METHODS: Searches against specified criteria yielded 19 papers based on 10 studies presenting data comparing dispersed housing with some kind of clustered housing (village ...


Keefe Lisa M   2009
Ixodes scapularis (Say) is the vector for Borrelia burgdorferi (Bb) the causative agent of Lyme disease (LD). The increased number and presence of ticks in the environment pose a significant health risk to people and many domestic animals including dogs, cats, and horses. This study characterized the distribution and expansion ...


Xie Minge   2009
This article develops a latent model and likelihoodbased inference to detect temporal clustering of events. The model mimics typical processes generating the observed data. We apply model selection techniques to determine the number of clusters, and develop likelihood inference and a Monte Carlo expectationmaximization algorithm to estimate model parameters, detect ...


Reich Brian J   2010
Quantile regression has emerged as a useful supplement to ordinary mean regression. Traditional frequentist quantile regression makes very minimal assumptions on the form of the error distribution and thus is able to accommodate nonnormal errors, which are common in many applications. However, inference for these models is challenging, particularly for ...


Lee JaeWoo   2009
The most massive globular cluster in the Milky Way, omega Centauri, is thought to be the remaining core of a disrupted dwarf galaxy, as expected within the model of hierarchical merging. It contains several stellar populations having different heavy elemental abundances supplied by supernovaea process known as metal enrichment. Although ...


Peng ShinLei   2009
In recent years, the temporal clustering analysis (TCA) method has been introduced to analyze functional MRI (fMRI) data without prior information about the activation patterns or experimental paradigms. It has been successfully applied to situations under which the timing of events of interest is not known. However, useful information regarding ...


Banks David L   2009
Complex data often arise as a superposition of data generated from several simpler models. The traditional strategy for such cases is to use mixture modelling, but it can be problematic, especially in higher dimensions. This paper considers an alternative approach, emphasizing data exploration and robustness to model misspecification. The strategy ...


Barber David   2009
Finding clusters of wellconnected nodes in a graph is a problem common to many domains, including social networks, the Internet and bioinformatics. From a computational viewpoint, finding these clusters or graph communities is a difficult problem. We use a clique matrix decomposition based on a statistical description that encourages clusters ...


Low John J   2009
Hydrothermal stability is a pertinent issue to address for many industrial applications where percent levels of water can be present at temperatures ranging from subambient to several hundred degrees. Our objective is to understand relative stabilities of MOF materials through experimental testing combined with molecular modeling. This will enable the ...


Mutwil Marek   2010
A vital quest in biology is comprehensible visualization and interpretation of correlation relationships on a genome scale. Such relationships may be represented in the form of networks, which usually require disassembly into smaller manageable units, or clusters, to facilitate interpretation. Several graphclustering algorithms that may be used to visualize biological ...


White Brian S   2009
Spectral clustering uses the global information embedded in eigenvectors of an interitem similarity matrix to correctly identify clusters of irregular shape, an ability lacking in commonly used approaches such as k means and agglomerative clustering. However, traditional spectral clustering partitions items into hard clusters, and the ability to instead generate ...


Collins Christopher   2009
While many data sets contain multiple relationships, depicting more than one data relationship within a single visualization is challenging. We introduce Bubble Sets as a visualization technique for data that has both a primary data relation with a semantically significant spatial organization and a significant set membership relation in which ...


Dang Edward K F   2009
Given a set of clusters, we consider an optimization problem which seeks a subset of clusters that maximizes the microaverage Fmeasure. This optimal value can be used as an evaluation measure of the goodness of clustering. For arbitrarily overlapping clusters, finding the optimal value is NPhard. We claim that a ...


Weiss Kenneth   2009
Volumetric datasets are often modeled using a multiresolution approach based on a nested decomposition of the domain into a polyhedral mesh. Nested tetrahedral meshes generated through the longest edge bisection rule are commonly used to decompose regular volumetric datasets since they produce highly adaptive crackfree representations. Efficient representations for such ...


Zawaira Alexander   2010
In this work, a model for the interaction between CYP2B4 and the FMN domain of rat P450oxidoreductase is built using as template the structure of a bacterial redox complex. Amino acid residues identified in the literature as cytochrome P450 (CYP)redox partner interfacial residues map to the interface in our model. ...


Durán Pacheco Gonzalo G Department of Public Health and Epidemiology, Interventions and Health Systems Unit, Swiss Tropical Institute, Basel, Switzerland.   2009
Many different methods have been proposed for the analysis of cluster randomized trials (CRTs) over the last 30 years. However, the evaluation of methods on overdispersed count data has been based mostly on the comparison of results using empiric data; i.e. when the true model parameters are not known. In ...


Pinzke Anders   2009
To fit recent data, e(+/) from dark matter (DM) needs a boosted annihilation rate. This may imply an observable level of gamma rays from nearby galaxy clusters for the Fermi satellite. Using EGRET data, we limit the minimum mass of DM substructures to be about 5x10(3) times larger than for ...


Shimatani Ichiro K   2010
Spatially explicit models relating to plant populations have developed little since Felsenstein (1975) pointed out that if limited seed dispersal causes clustering of individuals, such models cannot reach an equilibrium. This paper aims to resolve this issue by modifying the NeymanScott cluster point process. The new point processes are dynamic ...


Niu Jianwei   2009
Sizing based on 3D anthropometric data may lead to significant improvement in fitting comfort of wearing products. However, the required computational load is a common problem in 3D data processing. In a previous study, wavelet analysis was adopted to establish a multiresolution description of 3D anthropometric data to reduce computational ...


Salicr? Miquel   2009
Cluster analysis has proven to be a useful tool for investigating the association structure among genes in a microarray data set. There is a rich literature on cluster analysis and various techniques have been developed. Such analyses heavily depend on an appropriate (dis)similarity measure. In this paper, we introduce a ...


McNally Richard J Q   2009
The cause of primary biliary cirrhosis (PBC) is unclear. Both genetic and environmental factors are likely to contribute. Some studies have suggested that one or more infectious agents may be involved. To examine whether infections may contribute to the cause of PBC, we have analyzed for spacetime clustering using populationbased ...


Uehara Hiromichi   2009
Rotational transitions of HF that are important as a wavenumber standard have been analyzed by simultaneous fitting of the reported rotational and vibrationalrotational transitions plus rotational measurements of the present study with the nonBornOppenheimer effective Hamiltonian expressed with the optimal parameters, i.e., the determinable clusters of the expansion coefficients of ...


Spatial cluster detection for repeatedly measured outcomes while accounting for residential history.
Cook Andrea J AJ Group Health Research Institute, Seattle, WA 98101, USA.   2009
Spatial cluster detection has become an important methodology in quantifying the effect of hazardous exposures. Previous methods have focused on crosssectional outcomes that are binary or continuous. There are virtually no spatial cluster detection methods proposed for longitudinal outcomes. This paper proposes a new spatial cluster detection method for repeated ...


Jacquez Geoffrey M GM Department of Environmental Health Sciences, The University of Michigan, School of Public Health, Ann Arbor, 481092029, USA.   2009
Most disease clustering methods assume specific shapes and do not evaluate statistical power using the applicable geography, atrisk population, and covariates. Cluster Morphology Analysis (CMA) conducts power analyses of alternative techniques assuming clusters of different relative risks and shapes. Results are ranked by statistical power and false positives, under the ...


Wong WingCheong   2009
In general, a fuzzy neural network (FNN) is characterized by its learning algorithm and its linguistic knowledge representation. However, it does not necessarily interact with its environment when the training data is assumed to be an accurate description of the environment under consideration. In interactive problems, it would be more ...


Shen Ronglai   2009
The molecular complexity of a tumor manifests itself at the genomic, epigenomic, transcriptomic and proteomic levels. Genomic profiling at these multiple levels should allow an integrated characterization of tumor etiology. However, there is a shortage of effective statistical and bioinformatic tools for truly integrative data analysis. The standard approach to ...


Villarroel Luis   2009
A common situation in the biological and social sciences is to have data on one or more variables measured longitudinally on a sample of individuals. A problem of growing interest in these areas is the grouping of individuals into one of two or more clusters according to their longitudinal behavior. ...


Chatterjee Soumyadeep   2009
With the advent of the microarray technology, the field of life science has been greatly revolutionized, since this technique allows the simultaneous monitoring of the expression levels of thousands of genes in a particular organism. However, the statistical analysis of expression data has its own challenges, primarily because of the ...


Welf E S   2009
Regulation of proteinprotein interactions because of their spatial organisation in cells often shapes cell signalling responses to external stimuli, yet most current cell signalling models do not include spatial segregation of proteins beyond coarse control volumes like the cytosol or nucleus. A significant hindrance to spatial modelling of cell signalling ...


Rotman Z   2009
For a large class of repulsive interaction models, the Mayer cluster integrals can be transformed into a tridiagonal real symmetric matrix R_{mn} , whose elements converge to two constants. This allows for an effective extrapolation of the equation of state for these models. Due to a nearby (nonphysical) singularity on ...


Ye Jun   2009
Clustering of functional magnetic resonance imaging (fMRI) time serieseither directly or through characteristic features such as the crosscorrelation with the experimental protocol signalhas been extensively used for the identification of active regions in the brain. Both approaches have drawbacks; clustering of the time series themselves may identify voxels with similar ...


Du KL   2010
Clustering is a fundamental data analysis method. It is widely used for pattern recognition, feature extraction, vector quantization (VQ), image segmentation, function approximation, and data mining. As an unsupervised classification technique, clustering identifies some inherent structures present in a set of objects based on a similarity measure. Clustering methods can ...


Loobuyck M??lanie   2009
A crosssectional serological survey for Neospora caninum was carried out in Swedish beef cattle in order to estimate the seroprevalence and investigate any geographical patterns of the infection. Blood samples were collected from 2754 animals in 2130 herds and analysed for presence of antibodies to N. caninum. The study included ...


Snelder Ton   2009
Numerical clustering has frequently been used to define hierarchically organized ecological regionalizations, but there has been little robust evaluation of their performance (i.e., the degree to which regions discriminate areas with similar ecological character). In this study we investigated the effect of the weighting and treatment of input variables on ...


Ma Renjun   2009
In medical and health studies, heterogeneities in clustered count data have been traditionally modeled by positive random effects in Poisson mixed models; however, excessive zeros often occur in clustered medical and health count data. In this paper, we consider a threelevel random effects zeroinflated Poisson model for healthcare utilization data ...


Candel Math J J M   2009
Trials in which treatments induce clustering of observations in one of two treatment arms, such as when comparing group therapy with pharmacological treatment or with a waitinglist group, are examined with respect to the efficiency loss caused by varying cluster sizes. When observations are (approximately) normally distributed, treatment effects can ...


Lin PingChang   2009
MRI analysis of cartilage matrix may play an important role in early detection and development of therapeutic protocols for degenerative joint disease. Correlations between MRI parameters and matrix integrity have been established in many studies, but the substantial overlap in values observed for normal and for degraded cartilage greatly limits ...


Li Hai   2010
Recently, the tractbased white matter (WM) fiber analysis has been recognized as an effective framework to study the diffusion tensor imaging (DTI) data of human brain. This framework can provide biologically meaningful results and facilitate the tractbased comparison across subjects. However, due to the lack of quantitative definition of WM ...


Grieve Richard R Health Services Research Unit, London School of Hygiene and Tropical Medicine, London, UK.   2010
Costeffectiveness analyses (CEA) may be undertaken alongside cluster randomized trials (CRTs) where randomization is at the level of the cluster (for example, the hospital or primary care provider) rather than the individual. Costs (and outcomes) within clusters may be correlated so that the assumption made by standard bivariate regression models, ...


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