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The algorithm predicted missing expression values in gene regulatory networks, and could be applied to contextualize the network when all the expression values in two attractors are known.
Probes with missing expression values were excluded from analysis.
K Nearest Neighbor (KNN) imputation [52] was used to impute missing expression values in the source data sets, using the function impute.knn of the R package impute with default parameters (including k = 10).
Missing expression values were imputed via ImputeMissingValuesKNN module.
Missing expression values were imputed by k-nearest neighbors averaging (k = 10).
K-nearest neighbors [ 54] in the space of genes is used to impute missing expression values.
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Supposed DAB means the Euclidean distance between Gene A and Gene B and k genes are selected to estimate the missing expression value for Gene A. The weight of gene is: Given an example in Table 1, GeneC has a missing value in sample 1 and we compute DAC = 4.06, DBC = 3.24, and DDC = 6.94.
The EM_array approach constitutes one efficient method for restoring the missing expression gene values, with a lower estimation error level.
Therefore, RMI is a hybrid imputation method, and has several distinguished advantages over other methods for restoring the missing expression gene values.
Therefore, it is important to develop a strategy which could use the observed information fully for restoring the missing expression gene values.
Also, gene CG4109 was removed from this analysis because it was missing expression and significance values.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com