Normalization of RNA-sequencing (RNA-seq) data has proven essential to ensure accurate inference of expression levels. Here, we show that usual normalization approaches mostly account for sequencing ...

A topic that's often very confusing for beginners when using neural networks is data normalization and encoding. Because neural networks work internally with numeric data, binary data (such as sex, ...

We collected a unique pair of microRNA sequencing data sets for the same set of tumor samples; one data set was collected with and the other without uniform handling and balanced design. The former ...

AI training and inference are all about running data through models — typically to make some kind of decision. But the paths that the calculations take aren’t always straightforward, and as a model ...

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 ...