IMPACT OF DATA TRANSFORMATION ON THE PERFORMANCE OF DIFFERENT CLUSTERING METHODS AND CLUSTER NUMBER DETERMINATION STATISTICS FOR ANALYZING GENE EXPRESSION PROFILE DATA

We have assessed the impact of 13 different data transformation methods on the performance of four types of clustering methods (partitioning (K-mean), hierarchical distance (Average Linkage), multivariate normal mixture, and non-parametric kernel density) and four cluster number determination statistics (CNDS) (Pseudo F, Pseudo t2, Cubic Clustering Criterion (CCC), and Bayesian Information Criterion (BIC), using both simulated and real gene expression profile data. We found that Square Root, Cubic Root, and Spacing transformations have mostly positive impacts on the performance of the four types of clustering methods whereas Tukey\u27s Bisquare and Interquantile Range have mostly negative impacts. The impacts from other transformation methods are clustering method-specific and data type-specific. The performance of CNDS improves with appropriately transformed data. Multivariate Mixture Clustering and Kernel Density Clustering perform better than K-mean and Average Linkage in grouping both simulated and real gene expression profile data

Mixed model approaches for the identification of QTLs within a maize hybrid breeding program

Two outlines for mixed model based approaches to quantitative trait locus (QTL) mapping in existing maize hybrid selection programs are presented: a restricted maximum likelihood (REML) and a Bayesian Markov Chain Monte Carlo (MCMC) approach. The methods use the in-silico-mapping procedure developed by Parisseaux and Bernardo (2004) as a starting point. The original single-point approach is extended to a multi-point approach that facilitates interval mapping procedures. For computational and conceptual reasons, we partition the full set of relationships from founders to parents of hybrids into two types of relations by defining so-called intermediate founders. QTL effects are defined in terms of those intermediate founders. Marker based identity by descent relationships between intermediate founders define structuring matrices for the QTL effects that change along the genome. The dimension of the vector of QTL effects is reduced by the fact that there are fewer intermediate founders than parents. Furthermore, additional reduction in the number of QTL effects follows from the identification of founder groups by various algorithms. As a result, we obtain a powerful mixed model based statistical framework to identify QTLs in genetic backgrounds relevant to the elite germplasm of a commercial breeding program. The identification of such QTLs will provide the foundation for effective marker assisted and genome wide selection strategies. Analyses of an example data set show that QTLs are primarily identified in different heterotic groups and point to complementation of additive QTL effects as an important factor in hybrid performance

Impact of Optimized Breastfeeding on the Costs of Necrotizing Enterocolitis in Extremely Low Birthweight Infants

To estimate risk of NEC for ELBW infants as a function of preterm formula and maternal milk (MM) intake and calculate the impact of suboptimal feeding on NEC incidence and costs

Developing Risk Communication Skills: More Than Damage Management

Risk communication provides a methodology that enables an organization to respond effectively to issues or situations of a controversial nature. It is one important aspect of risk management

Television news practices and policies for reporting civil disorders in Kansas and Kansas City, Missouri

Digitized by Kansas Correctional Industrie

Loss functions for estimation of extrema with an application to disease mapping

It is often of interest to find the maximum or near maxima among a set of vector-valued parameters in a statistical model; in the case of disease mapping, for example, these correspond to relative-risk hotspots where public-health intervention may be needed. The general problem is one of estimating nonlinear functions of the ensemble of relative risks, but biased estimates result if posterior means are simply substituted into these nonlinear functions. The authors obtain better estimates of extrema from a new, weighted ranks squared error loss function. The derivation of these Bayes estimators assumes a hidden-Markov random-field model for relative risks, and their behaviour is illustrated with real and simulated data

Mapping rates associated with polygons

Suppose that geographic data under investigation are rates associated with polygons. For example, disease incidence, mortality, and census undercount data may be displayed as rates. Spatial analysis of data of this sort can be handled very naturally through Bayesian hierarchical statistical modeling, where there is a measurement process at the first level, an explanatory process at the second level, and a prior probability distribution on unknowns at the third level. In our paper, we shall feature epidemiological data, specifically disease-incidence rates, and the \u27polygons\u27 referred to in the title are typically states or counties

You’re not alone: discovering the power of sharing life narratives as academic women

This chapter offers an auto-ethnographic account of my experiences of working as a critical social work educator and trade unionist in contemporary academia. In doing so it provides insight into the increasingly neoliberalised higher education sector, and some of the challenges this context can pose for academics who adopt a critical stance and conceptualise education as having the potential to contribute to a more socially just, equitable and democratic society. The chapter reflects on a managerial system that is wholly implicated in supporting and normalising bullying. The paper seeks to contribute to critical scholarship on higher education by theorising a considered, ethical response to neoliberal managerialism within universities. Critical pedagogy, collective action and collegial solidarity are suggested as practices of agency and resistance