Carlos Mendes Carvalho

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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 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)
The concept of sparsity is more and more central to practical data analysis and inference with increasingly high-dimensional data. Gene expression genomics is a key example context. As part of a series of projects that has developed Bayesian methodology for large-scale regression, ANOVA and latent factor models, we have extended traditional Bayesian(More)
Recent studies have emphasized the importance of pathway-specific interpretations for understanding the functional relevance of gene alterations in human cancers. Although signaling activities are often conceptualized as linear events, in reality, they reflect the activity of complex functional networks assembled from modules that each respond to input(More)
Human disease studies using DNA microarrays in both clinical/observational and experimental/controlled studies are having increasing impact on our understanding of the complexity of human diseases. A fundamental concept is the use of gene expression as a "common currency" that links the results of in vitro controlled experiments to in vivo observational(More)
We describe a strategy for the analysis of experimentally derived gene expression signatures and their translation to human observational data. Sparse multivariate regression models are used to identify expression signature gene sets representing downstream biological pathway events following interventions in designed experiments. When translated into in(More)
Bayesian dynamic linear models (DLMs) (West and Harrison, 1997) are used for analysis and prediction of time series of increasing dimension and complexity in many applied fields. The time-varying regression structure, or state-space structure, and the sequential nature of DLM analysis flexibly allows for the creation and routine use of interpretable models(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)