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Compounds in the dataset were converted from Tripos mol2 format to MDL mol format using Open Babel.
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For each subject, the T1-weighted, anatomical 3-D MRI dataset was converted from Dicom format into.mgz format, intensity variations were corrected, and a normalized intensity image was created.
In DAnTE, all data from each dataset were converted into a log base 2 value and each of the triplicate instrument replicates of the same sample were then linear regression normalized using a reference line composed of values obtained from the median value obtained from each of the three replicates.
Gene annotations from each dataset were converted into UniGene Cluster IDs (UCIDs, Build 161) using the SOURCE database[ 43], and multiple occurrences of a UCID were collapsed by taking the median value for that ID within each experiment and platform, which resulted in ~2800 genes having expression data in all three datasets.
Families from the T1DGC dataset were converted into cases and pseudo-controls using the AFBAC method (Thomson 1995).
The 3D interaction information extracted from the training dataset is converted into a 2D table (reference contact matrix, CM) having H3 residues in the columns and environment residues in the rows.
Each dataset was converted into raster data format.
These dataset is converted into a stream for the testing process.
The CityGML dataset was converted to OBJ with the CityGML2OBJs Python script, which preserves the semantic description of surfaces by creating multiple OBJ datasets [35].
The final dataset was converted to be modeled using WEKA.
The final dataset was converted into SPSS format.
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