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As one of the data processing steps recommended in [ 13], logarithmic transformation are applied on microarray datasets: base 10 log for data derived from oligonucleotide (Affymetrix) platform and base 2 log for data derived from cDNA (two-color) platform.
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Experiments are conducted using four well-known benchmark datasets based on ACFs, ACCFs, and Locally Decorrelated Channel Features (LDCFs).
In total, seven datasets based on these five surveys were analyzed in this work.
This process converts crisp inputs into fuzzy input datasets based on the membership functions.
Two separate datasets based on FICM and SICS criteria were gathered.
The service produces raw or calibrated data organized into datasets based on each device rather than events.
And maximum likelihood method was used to estimate the reliability of protein interaction datasets based on the three attributes.
In this study, FFS was run for each of the four benchmark datasets based on the corresponding IFS result.
For this to be a standalone test, we then select the datasets based on the variance in the dataset and only use datasets with high variations.
First, the datasets were normalized using quantile normalization to ensure that inherent large-scale expression differences in the datasets based on different sources and laboratories were minimized.
We found that datasets based principally on CATH domains (CATH-Pfam and CATH) performed less well than those based on Pfam domains (see Supplementary Figure S1).
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