TY - JOUR AU - Urbanowicz, Ryan J. AU - Kiralis, Jeff AU - Fisher, Jonathan M. AU - Moore, Jason H. PY - 2012 DA - 2012/09/26 TI - Predicting the difficulty of pure, strict, epistatic models: metrics for simulated model selection JO - BioData Mining SP - 15 VL - 5 IS - 1 AB - 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 alone can influence the detectability of a model. In order to design a simulation study which efficiently takes architecture into account, a reliable metric is needed for model selection. SN - 1756-0381 UR - https://doi.org/10.1186/1756-0381-5-15 DO - 10.1186/1756-0381-5-15 ID - Urbanowicz2012 ER -