Learn More
The tumor microenvironment has a significant impact on tumor development. Two important determinants in this environment are hypoxia and lactic acidosis. Although lactic acidosis has long been recognized as an important factor in cancer, relatively little is known about how cells respond to lactic acidosis and how that response relates to cancer phenotypes.(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)
Tumor microenvironmental stresses, such as hypoxia and lactic acidosis, play important roles in tumor progression. Although gene signatures reflecting the influence of these stresses are powerful approaches to link expression with phenotypes, they do not fully reflect the complexity of human cancers. Here, we describe the use of latent factor models to(More)
There is great potential for host-based gene expression analysis to impact the early diagnosis of infectious diseases. In particular, the influenza pandemic of 2009 highlighted the challenges and limitations of traditional pathogen-based testing for suspected upper respiratory viral infection. We inoculated human volunteers with either influenza A(More)
We develop a sticky hidden Markov model (HMM) with a Dirichlet distribution (DD) prior, motivated by the problem of analyzing comparative genomic hybridization (CGH) data. As formulated the sticky DD-HMM prior is employed to infer the number of states in an HMM, while also imposing state persistence. The form of the proposed hierarchical model allows(More)
Point process data are commonly observed in fields like healthcare and the social sciences. Designing predictive models for such event streams is an under-explored problem, due to often scarce training data. In this work we propose a multi-task point process model, leveraging information from all tasks via a hierarchical Gaussian process (GP). Nonparametric(More)
The widespread use of DNA microarray gene expression technology offers the potential to substantially increase our understanding of cellular pathways and apply that knowledge in clinical as well as basic biological studies. In human biology, there are two common types of study performed with expression arrays. The first involves samples derived from cloned(More)
BACKGROUND The biological phenotype of a cell, such as a characteristic visual image or behavior, reflects activities derived from the expression of collections of genes. As such, an ability to measure the expression of these genes provides an opportunity to develop more precise and varied sets of phenotypes. However, to use this approach requires(More)
INTRODUCTION Polymorphisms near the IL28B gene (e.g. rs12979860) encoding interferon λ3 have recently been associated with both spontaneous clearance and treatment response to pegIFN/RBV in chronic hepatitis C (CHC) patients. The molecular consequences of this genetic variation are unknown. To gain further insight into IL28B function we assessed the(More)
SUMMARY A non-parametric Bayesian factor model is proposed for joint analysis of multi-platform genomics data. The approach is based on factorizing the latent space (feature space) into a shared component and a data-specific component with the dimensionality of these components (spaces) inferred via a beta-Bernoulli process. The proposed approach is(More)