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Table 4 Average imputation accuracies on the three density mouse datasets

From: Fast accurate missing SNP genotype local imputation

Methods: Npute NN WNN fPH SVM NeuN MC BL
0.01-0.5% .8363 .8977 .8960 .8655 .8534 .8357 .8278 .8106
0.01-1% .8554 .9068 .9079 .8898 .8517 .8371 .8322 .8088
0.01-2% .8542 .9023 .9006 .8932 .8588 .8445 .8322 .8121
0.01-5% .8462 .8940 .8955 .8898 .8573 .8436 .8347 .8162
0.01-10% .8406 .8861 .8865 .8854 .8470 .8354 .8283 .8120
0.01-20% .8281 .8668 .8639 .8712 .8366 .8268 .8231 .8118
0.1-0.5% .8708 .9247 .9236 .9237 .8831 .8680 .8647 .8215
0.1-1% .8685 .9283 .9267 .9296 .8850 .8697 .8634 .8203
0.1-2% .8672 .9241 .9240 .9269 .8810 .8636 .8578 .8159
0.1-5% .8655 .9201 .9212 .9252 .8796 .8611 .8571 .8160
0.1-10% .8617 .9140 .9139 .9212 .8741 .8553 .8527 .8158
0.1-20% .8541 .9015 .8986 .9112 .8598 .8426 .8426 .8136
1-0.5% .8825 .9405 .9377 .9434 .9032 .8898 .8723 .8152
1-1% .8814 .9392 .9373 .9432 .9023 .8896 .8742 .8169
1-2% .8806 .9381 .9364 .9426 .8986 .8885 .8730 .8171
1-5% .8788 .9358 .9334 .9408 .6215 .8845 .8703 .8166
1-10% .8763 .9317 .9280 .9375 .8377 .8777 .8657 .8167
1-20% .8695 .9223 .9156 .9290 .7365 .8621 .8553 .8151
  1. Average imputation accuracies on the three density mouse datasets. At each missing rate, the highest accuracy is in bold. ‘fPH, NeuN’ stand for ‘fastPHASE, NeuralNet’, respectively.