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(ii) It can handle missing values of drug susceptibility measurements, which enables us not to discard data points with missing outputs, leading to larger data collections.
In [31], OptShrink has been shown to have improved performance over singular value thresholding (SVT) in the recovery of data with missing entries.
Deletion imputation We simply discard all data with missing values.
46 Second, we replaced missing data points with estimates in some instances, which introduced further uncertainty.
This genetic map contains about 152,000 pairwise genetic interaction measurements with about 29% missing entries in data matrix.
Spot signals were filtered according to absolute signal intensity (threshold of ≥2 times versus control spots), viable spots numbers (≥2) and missing data (spot series with missing data in ≥25 % of the samples were discarded).
Data points with detection p-values > 0.001 were set to missing.
To cope with this incomplete data issue, we introduce a statistical data analysis procedure that enables to estimate and impute missing entries in the within-subject design table.
The quality of the data entry is verified by automated checking for erroneous or missing entries.
Low-rank tensor completion addresses the task of filling in missing entries in multi-dimensional data.
First, we discarded clones with missing data in more than 5% of the arrays and applied a base-2 logaritransformationmatoon thethexpressionon data.
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