Extracting biomedical information from large metabolomic datasets by multivariate data analysis is of considerable complexity. Common challenges include among others screening for differentially ...

In histopathology, where tissues are studied under the microscope to understand and diagnose diseases, stains represent a ...

For example, it's better to normalize data before encoding because encoding generates many additional numeric columns which makes it a bit more complicated to normalize the original numeric data.

When a normalization step is included, variability is reduced, data comparisons are made easier, and statistical importance and confidence in the data are improved. While several techniques are ...

For example, it's better to normalize data before encoding because encoding generates many additional numeric columns which makes it a bit more complicated to normalize the original numeric data.

Several analytic methods have been proposed for normalizing sequencing depth. Earlier methods were based mostly on the scaling strategy, which calculates a scaling factor (eg, the total number of ...

This requirement to provide clearer evidence of tangible improvements increases the pressure on data center managers. Most managers already use a myriad of software and reporting tools to maintain ...

Comparison of expression data requires normalization. The optimum normalization method depends on sample type, with the most common being to normalize to reference genes. It is critical to select ...