INI Programme SCH Workshop - High Dimensional Statistics in Biology

High Dimensional Statistics in Biology

31 March to 4 April 2008

Isaac Newton Institute for Mathematical Sciences, Cambridge, UK

Organisers: Peter Bickel (UC Berkeley), Ewan Birney (EBI), Wolfgang Huber (EBI) and Richard Durbin (Sanger Institute)

in association with the Newton Institute programme Statistical Theory and Methods for Complex, High-Dimensional Data (7 January to 27 June 2008)

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Accepted Posters:

Name Title Abstract Poster
Abnizova, I SVM application of `motif surprise' kernels for regulatory region detection Abstract
Chiang, T Combining protein interactions Abstract
Ding, Y Feature selection and classification based on K-nearest neighbour patterns Abstract
Edelman, E Modeling cancer progression via pathway dependencies Abstract
Ernst,J Reconstructing dynamic regulatory maps Abstract
Gilbert, HN Resampling-based Multiple Hypothesis Testing: New Developments in the R package multtest Abstract Poster
Gräf, S Comparing methods for the analysis of genome wide chIP data Abstract
Hageman, RS Bayesian methods for large-scale dynamical metabolic systems: modeling, prediction and sensitivity analysis Abstract
Jones, B Experiences fitting Gaussian graphical models to microarray data using the lasso Abstract Poster
Kar, R, Khan, ZA and Mandal, A Antibacterial properties of common Indian ant solenopsis warfaria Abstract Poster
Kim, S RAN-aCGH: R CUI tools for analysis and visualization of an array-CGH experiment Abstract Poster
McGettigan, P Transcriptional analysis of a schizophrenia endophenotype in rat Abstract
Okoniewski, MJ X: MAP and exonmap - tools for high-throughput transcriptomic analyses Abstract
Sang, H Interpreting self organising maps through space-time data models Abstract
Schmidberger, MS Parallelised preprocessing algorithms for high-density oligonucleotide array data Abstract Poster
Shojaie, A Analysis of gene sets based on the underlying gene regulatory network Abstract Poster
Timofeev, N Linkage disequilibrium filter in genome wide association studies Abstract
Walter, K New algorithms for detecting structural variation Abstract
Yasrebi, H Breast cancer survival prediction using merged gene expression data sets Abstract
Youn, A Learning transcriptional regulatory modules from the integration of chIP-chip and gene expression data Abstract

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