TY - JOUR AU - Cartier, Kevin C. AU - Baechle, Daniel PY - 2005 DA - 2005/12/30 TI - An artificial neural network for estimating haplotype frequencies JO - BMC Genetics SP - S129 VL - 6 IS - 1 AB - The problem of estimating haplotype frequencies from population data has been considered by numerous investigators, resulting in a wide variety of possible algorithmic and statistical solutions. We propose a relatively unique approach that employs an artificial neural network (ANN) to predict the most likely haplotype frequencies from a sample of population genotype data. Through an innovative ANN design for mapping genotype patterns to diplotypes, we have produced a prototype that demonstrates the feasibility of this approach, with provisional results that correlate well with estimates produced by the expectation maximization algorithm for haplotype frequency estimation. Given the computational demands of estimating haplotype frequencies for 20 or more single-nucleotide polymorphisms, the ANN approach is promising because its design fits well with parallel computing architectures. SN - 1471-2156 UR - https://doi.org/10.1186/1471-2156-6-S1-S129 DO - 10.1186/1471-2156-6-S1-S129 ID - Cartier2005 ER -