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The development of an oncogenic state is a complex process involving the accumulation of multiple independent mutations that lead to deregulation of cell signalling pathways central to the control of cell growth and cell fate. The ability to define cancer subtypes, recurrence of disease and response to specific therapies using DNA microarray-based gene(More)
We describe studies in molecular profiling and biological pathway analysis that use sparse latent factor and regression models for microarray gene expression data. We discuss breast cancer applications and key aspects of the modeling and computational methodology. Our case studies aim to investigate and characterize heterogeneity of structure related to(More)
We discuss the theoretical structure and constructive methodology for large-scale graphical models, motivated by their potential in evaluating and aiding the exploration of patterns of association in gene expression data. The theoretical discussion covers basic ideas and connections between Gaussian graphical models, dependency networks and specific classes(More)
BACKGROUND Correlation of risk factors with genomic data promises to provide specific treatment for individual patients, and needs interpretation of complex, multivariate patterns in gene expression data, as well as assessment of their ability to improve clinical predictions. We aimed to predict nodal metastatic states and relapse for breast cancer(More)
We extend recently introduced latent threshold dynamic models to include dependencies among dynamic latent factors underlying multivariate volatility. With an ability to induce time-varying sparsity into factor loadings, these models now also allow time-varying correlations among factors; this may be exploited to improve volatility forecasts. We couple(More)
We discuss the implementation, development and performance of methods of stochastic computation in Gaussian graphical models, with a particular interest on the scalability of MCMC and other stochastic search methods with dimension. Our perspective is that of highdimensional model search – we are interested in exploring the complex, high-dimensional spaces(More)
In Bayesian density estimation and prediction using Dirichlet process mixtures of standard, exponential family distributions, the precision or total mass parameter of the mixing Dirichlet process is a critical hyperparameter that strongly influences resulting inferences about numbers of mixture components. This note shows how, with respect to a flexible(More)
We describe methods for applying Monte Carlo filtering and smoothing for estimation of unobserved states in a nonlinear state-space model. By exploiting the statistical structure of the model, we develop a Rao–Blackwellized particle smoother. Due to the lengthy nature of real signals, we suggest processing the data in blocks, and a block-based smoother(More)