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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.