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Table 2 Average imputation accuracies on the three density human datasets

From: Fast accurate missing SNP genotype local imputation

Methods fPH NN WNN SVM NeuN MC BL
0.01-0.5% .6427 .6456 .6225 .6499 .6431 .6561 .6495
0.01-1% .6418 .6279 .6119 .6353 .6338 .6399 .6384
0.01-2% .6707 .6447 .6091 .6503 .6474 .6474 .6477
0.01-5% .6656 .6171 .5968 .6452 .6415 .6474 .6449
0.01-10% .6683 .6113 .5927 .6506 .6472 .6510 .6513
0.01-20% .6658 .6016 .5776 .6492 .6465 .6515 .6536
0.1-0.5% .7692 .7167 .7348 .7412 .6707 .7032 .6484
0.1-1% .7712 .7087 .7294 .7340 .6691 .6999 .6501
0.1-2% .7778 .7081 .7311 .7399 .6753 .7092 .6577
0.1-5% .7741 .6993 .7176 .7345 .6714 .7043 .6556
0.1-10% .7684 .6908 .7048 .7252 .6701 .7011 .6549
0.1-20% .7547 .6742 .6804 .7105 .6652 .6944 .6548
1-0.5% .9548 .8836 .9094 .9036 .7854 .7732 .6493
1-1% .9537 .8822 .9077 .9028 .7826 .7722 .6493
1-2% .9520 .8796 .9036 .9006 .7820 .7713 .6495
1-5% .9502 .8755 .8919 .8948 .7774 .7679 .6503
1-10% .9462 .8673 .8736 .8833 .7689 .7611 .6494
1-20% .9373 .8481 .8391 .8579 .7526 .7488 .6493
  1. Average imputation accuracies on the three density human datasets. At each missing rate, the highest accuracy is in bold. ‘fPH, NeuN’ stand for ‘fastPHASE, NeuralNet’, respectively.