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 ...

Normalization techniques have become integral to the training of deep neural networks, serving to stabilise learning dynamics, accelerate convergence and improve generality. At their core, these ...

Understanding data encoding and normalization is an absolutely essential skill when working with neural networks. James McCaffrey walks you through what you need to know to get started. Dealing with ...

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 ...

Because neural networks natively process numeric data, the sex, location type and political affiliation data must be encoded to numeric values. For example, one possibility for sex is to encode male ...