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Your search keyword '"Wild, David L"' showing total 4 results
Start Over Author "Wild, David L" Language english Publisher springer nature
4 results on '"Wild, David L"'

1. ParticleMDI: particle Monte Carlo methods for the cluster analysis of multiple datasets with applications to cancer subtype identification.

Author

Cunningham, Nathan, Griffin, Jim E., and Wild, David L.

Abstract

We present a novel nonparametric Bayesian approach for performing cluster analysis in a context where observational units have data arising from multiple sources. Our approach uses a particle Gibbs sampler for inference in which cluster allocations are jointly updated using a conditional particle filter within a Gibbs sampler, improving the mixing of the MCMC chain. We develop several approaches to improving the computational performance of our algorithm. These methods can achieve greater than an order-of-magnitude improvement in performance at no cost to accuracy and can be applied more broadly to Bayesian inference for mixture models with a single dataset. We apply our algorithm to the discovery of risk cohorts amongst 243 patients presenting with kidney renal clear cell carcinoma, using samples from the Cancer Genome Atlas, for which there are gene expression, copy number variation, DNA methylation, protein expression and microRNA data. We identify 4 distinct consensus subtypes and show they are prognostic for survival rate ( p < 0.0001 ). [ABSTRACT FROM AUTHOR]

Published
2020
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3. A Robust Bayesian Two-Sample Test for Detecting Intervals of Differential Gene Expression in Microarray Time Series.

Author

Stegle, Oliver, Denby, Katherine, Wild, David L., Ghahramani, Zoubin, and Borgwardt, Karsten M.

Abstract

Understanding the regulatory mechanisms that are responsible for an organism΄s response to environmental changes is an important question in molecular biology. A first and important step towards this goal is to detect genes whose expression levels are affected by altered external conditions. A range of methods to test for differential gene expression, both in static as well as in time-course experiments, have been proposed. While these tests answer the question whether a gene is differentially expressed, they do not explicitly address the question when a gene is differentially expressed, although this information may provide insights into the course and causal structure of regulatory programs. In this article, we propose a two-sample test for identifying intervals of differential gene expression in microarray time series. Our approach is based on Gaussian process regression, can deal with arbitrary numbers of replicates and is robust with respect to outliers. We apply our algorithm to study the response of Arabidopsis thaliana genes to an infection by a fungal pathogen using a microarray time series dataset covering 30,336 gene probes at 24 time points. In classification experiments our test compares favorably with existing methods and provides additional insights into time-dependent differential expression. [ABSTRACT FROM AUTHOR]

Published
2009
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4. Expectations from Structural Genomics Revisited: An Analysis of Structural Genomics Targets.

Author

Saqi, Mansoor A.S. and Wild, David L.

Subjects
*GENOMICS, *PROTEIN folding, *AMINO acid sequence, *PROTEIN analysis, *PROTEIN research
Abstract

Background: Current structural genomics projects are being driven by two main goals; to produce a representative set of protein folds that could be used as templates for comparative modeling purposes, and to provide insight into the function of the currently unannotated protein sequences. Such projects may reveal that a newly determined protein structure shares structural similarity with a previously observed structure or that it is a novel fold. The manner in which structure can be used to suggest the function of a protein will depend on the number and diversity of homologous sequences and the extent to which these sequences are functionally characterized. Method and results: Using sequence searching methods, we analyzed structural genomics target sequences to ascertain if they were members of functionally characterized protein families, protein families of unknown function, or orphan sequences. This analysis provided an indication of what could be expected to emerge from structural genomics projects. Matches were found to approximately 25% of the current functionally unannotated protein families in the PFAM database (protein families database of alignments and hidden Markov models). The 16% of strict orphan sequences will be the most problematic if their structures reveal novel folds. However, out of the remaining target sequences that match families whose members are largely of unknown function, 28% are particularly interesting in that they are part of protein families with considerable sequence diversity. Conclusion:The determination of a new structure of a member of these families is likely to offer considerable insight into possible functional roles of these proteins even if it is a new fold. Mapping the sequence conservation onto the structure may reveal functionally important residues for further study by experimental methods. [ABSTRACT FROM AUTHOR]

Published
2005
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