Predicting the difficulty of pure, strict, epistatic models: metrics for simulated model selection
Abstract
Background Algorithms designed to detect complex genetic disease associations are initially evaluated using simulated datasets. Typical evaluations vary constraints that influence the correct detection of underlying models (i.e. number of loci, heritability, and minor allele frequency). Such studies neglect to account for model architecture (i.e. the unique specification and arrangement of penetrance values comprising the genetic model), which...
Paper Details
Title
Predicting the difficulty of pure, strict, epistatic models: metrics for simulated model selection
Published Date
Sep 26, 2012
Journal
Volume
5
Issue
1
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