> ROYAL STATISTICAL SOCIETY MEDICAL SECTION
>
> Tuesday 29 November 2005 2.00-5.00 (tea at 3.10).
>
>
>
> To be held at the RSS, 12 Errol Street, London EC1Y 8LX
>
> (directions http://www.rss.org.uk/about/direction.html)
>
> Pre-registration is recommended - please email [log in to unmask]
> <mailto:[log in to unmask]> <mailto:[log in to unmask]> or tel. 020
> 7638 8998 to register.
>
>
>
> ‘Statistics in the post-genomic age’
>
>
> A half-day meeting of presentations
>
>
>
> 2.00 Wally Gilks. MRC Biostatistics Unit, Cambridge.
>
> 2.35 Heather J. Cordell, Department of Medical Genetics, University of
> Cambridge.
>
> 3.10 Tea
>
> 3.40 Sylvia Richardson, Imperial College, London.
>
> 4.20 Jenny Barrett, Division of Genetic Epidemiology, University of
> Leeds.
>
> 5.00 Close
>
>
>
> Titles and abstracts
>
>
> Statistical Bioinformatics: an overview.
>
> Wally Gilks. MRC Biostatistics Unit, Cambridge.
>
> The rapid expansion of DNA, protein, gene expression and other genomic
> databases has spawned the field of bioinformatics. Work in this field
> spans the development of databases and algorithms, and research in
> basic biology, genetics and medicine. I will briefly review the state
> of the art, focussing on current and potential areas of involvement of
> statisticians.
>
>
> Regression methods for SNP data in case/control and family studies.
>
> Heather J. Cordell, Department of Medical Genetics, University of
> Cambridge.
>
> Here I discuss methods of design and analysis of genetic association
> studies. There are many similarities between genetic association
> studies and classical epidemiological studies of environmental risk
> factors, but there are also issues specific to studies of genetic risk
> factors such as the use of certain family-based designs, accounting
> for different underlying genetic mechanisms and the impact of
> population history.
>
>
> Statistical analysis of gene expression data
>
> Sylvia Richardson, Imperial College, London.
>
> The powerful technology of cDNA or oligonucleotide microarray makes it
> possible to study simultaneously the expression of thousands of genes
> in different samples. To interpret and model this vast body of data
> poses interesting statistical challenges. Issues of signal extraction,
> normalisation and how to find groups of genes that are differentially
> expressed will be discussed. The flexibility and benefits of using a
> Bayesian hierarchical modelling framework to perform such analyses
> will be illustrated.
>
>
> Statistical issues in clinical proteomics
>
> Jenny Barrett, Division of Genetic Epidemiology, University of Leeds.
>
> Proteomics concerns the identification and characterisation of
> expressed proteins in an organism. The subject will be introduced,
> focusing on SELDI mass spectrometry profiles. Statistical issues in
> experimental design, pre-processing and peak detection will be
> discussed, before moving on to identification of differences between
> peak profiles in different sample groups. Methods will be illustrated
> with clinically-motivated examples.
>
>
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