|
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
|
- 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.
