Microarrays have set the stage for an explosion of large-scale expression data, driven by a diversity of genome sequencing projects. The technology has already demonstrated its applications in ...

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 ...

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 ...

Dr. James McCaffrey of Microsoft Research uses a full code sample and screenshots to show how to programmatically normalize numeric data for use in a machine learning system such as a deep neural ...

There are many types of experimental methods that often use normalization to fix the differences induced by factors other than what is immediately being analyzed. In particular, normalization can be ...

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 ...