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Table 3 Average imputation accuracies on the two cattle datasets

From: Fast accurate missing SNP genotype local imputation

Methods: fPH NN WNN SVM NeuN MC BL
hd-0.5% .9151 .8701 .8748 .8538 .6518 .8012 .5449
hd-1% .9209 .8695 .8707 .8563 .6495 .7991 .5432
hd-2% .9174 .8624 .8599 .8484 .6437 .7933 .5412
hd-5% .9123 .8482 .8436 .8362 .6336 .7817 .5379
hd-10% .8968 .8232 .8344 .8057 .6204 .7771 .5378
hd-20% .8831 .7951 .8032 .7782 .6072 .7548 .5352
ld-0.5% .9643 .8618 .8705 .8765 .6817 .7269 .6563
ld-1% .9627 .8601 .8674 .8732 .6799 .7270 .6527
ld-2% .9616 .8571 .8636 .8704 .6796 .7265 .6552
ld-5% .9598 .8480 .8468 .8577 .6765 .7230 .6532
ld-10% .9566 .8328 .8209 .8395 .6740 .7169 .6542
ld-20% .9492 .7951 .7686 .8029 .6680 .7110 .6528
  1. Average imputation accuracies on the two cattle datasets, the high-density one is 700K and the low-density one is 60K. At each missing rate, the highest accuracy is in bold. ‘fPH, NeuN’ stand for ‘fastPHASE, NeuralNet’, respectively.