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References in periodicals archive ?
First, most laboratories are performing targeted sequencing of tumor-only specimens to detect SNVs and small insertions and deletions with a reported lower limit of detection of 5% to 10% VAF. Second, the testing is primarily performed using amplicon-based, predesigned commercial kits with benchtop sequencers.
Homozygous mutations of JAK2 (V617F) (with a VAF value >50%) are more common in PV than in ET (78,79).
This along with variable air flow (VAF) fans offers new possibilities for DX-DOAS with substantial energy savings potential when coupled with occupancy demand control ventilation (DCV).
The KITD816V mutation burden (VAF) was calculated by dividing the number of mutated KIT D816V copies by the total number of KIT copies, and VAF results were expressed as percent mutant alleles.
Data type Model parameters Minimum value Maximum value Calcium carbonate 3% 0.00 10.00 Calcium carbonate 5% 0.00 7.50 ANN-1 input Calcium carbonate 10% 0.00 10.00 Fiber content 0.00 2.50 Sand blasting 0.00 1.00 Calcium carbonate 3% 0.00 10.00 Calcium carbonate 5% 0.00 7.50 ANN-2 input Calcium carbonate 10% 0.00 10.00 Fiber content 0.00 2.50 Polishing 0.00 1.00 Output BPN 56.00 73.00 Data type Mean Standard deviation 2.81 3.72 1.94 2.74 ANN-1 input 1.94 3.55 1.87 0.63 0.38 0.49 2.81 3.72 1.94 2.74 ANN-2 input 1.94 3.55 1.87 0.63 0.63 0.49 Output 64.30 3.45 Table 4: Performance indices ([R.sup.2], RMSE, MAE, and VAF) of the ANN and MR models.
Method Parameter description Parameter value Number of hidden layers 1 SBPC Number of neurons in hidden layers 15 Learning rate 0.01 Maximum number of epochs to train 5000 Number of hidden layers 1 Number of neurons in hidden layers 15 ALRBPC Learning rate 0.01 Ratio to increase learning rate 1.05 Ratio to decrease learning rate 0.7 Maximum number of epochs to train 1000 GRNNC Spread parameter 0.5 FC Fuzzy parameter a 0.2 Fuzzy parameter c 0.8 Fuzzy parameter a 0.2 FC-VAF Fuzzy parameter c 0.8 Level of wavelet decomposition 2 Number of VAF 4 Table 2: Comparisons of different scene classification methods.
Among them, we cited the Mean Square Error (MSE) test, Root Mean Square Error (RMSE), and the Variance Accounting For (VAF) test.
Calling of somatic mutations was achieved using an algorithm that excluded synonymous mutations, variants located within intronic or untranslated regions, and those present at a variant allele frequency (VAF) of <5%.
To evaluate the quality of the extracted synergies, the variance accounted for (VAF) is usually used to calculate the percentage of variability in the sEMG dataset that is accounted for by the extracted synergies.