This Perspective examines single-cell RNA-seq data challenges and the need for normalization methods designed specifically for single-cell data in order to remove technical biases.

Summary In general, data normalization establishes a foundation for achieving semantic interoperability and creates an infrastructure that enables data sharing and aggregation.

The Data Science Lab Data Prep for Machine Learning: Normalization Dr. James McCaffrey of Microsoft Research uses a full code sample and screenshots to show how to programmatically normalize numeric ...

This process of normalization involves converting data from its proprietary format so it can be used by any integrated solution, such as DCIM, BMS, ITSM, ERP and other applications that provide ...

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, ...

This paper considers inference for both spatial lattice data with possibly irregularly shaped sampling region and non-lattice data, by extending the recently proposed self-normalization (SN) approach ...

Someone implementing the model in a data center, for example, may be unaware of any normalization. Data centers typically employ hardware that can run inference on a model at full 32-bit ...

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