The application of naive Bayes model averaging to predict Alzheimer's disease from genome-wide data.
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.
Author(s): Wei, Wei, Visweswaran, Shyam, Cooper, Gregory F
DOI: 10.1136/amiajnl-2011-000101