PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Bayesian construction of perceptrons to predict phenotypes from 584K SNP data.
Luc Janss and Bert Kappen
In: PASCAL Computational Statistics Workshop, June 2009, London, UK.

Abstract

Introduction ● Genetic prediction still difficult ● But also done very simple, testing single predictors.... ● Here: Bayesian multivariate model building ● Based on “Variable Selection” George & McCullogh 1993 ● Constructs complete “perceptrons”: the simultaneous bitpattern for selected predictors ● Example: human data 632 phenotypes + orginally 710K SNPs (p/n >1000) ● > Using 3 chromosomes, 104K SNPs ● > Cross validation using 80:20 split

EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
Theory & Algorithms
ID Code:6880
Deposited By:Bert Kappen
Deposited On:09 April 2010