The application of naive Bayes model averaging to predict Alzheimer's disease from genome-wide data.

ABSTRACT:

Objective

Predicting patient outcomes from genome-wide measurements holds significant promise for improving clinical care. The large number of measurements (eg, single nucleotide polymorphisms (SNPs)), however, makes this task computationally challenging. This paper evaluates the performance of an algorithm that predicts patient outcomes from genome-wide data by efficiently model averaging over an exponential number of naive Bayes (NB) models.

Design

This model-averaged naive Bayes (MANB) method was applied to predict late onset Alzheimer's disease in 1411 individuals who each had 312,318 SNP measurements available as genome-wide predictive features. Its performance was compared to that of a naive Bayes algorithm without feature selection (NB) and with feature selection (FSN

SUBMITTER: Wei W 

PROVIDER: S-EPMC3128400 | biostudies-literature | 2011 Jul-Aug

REPOSITORIES: biostudies-literature