Single-cell RNA sequencing (scRNA-seq) has transformed the field of transcriptomics by making it possible for researchers to address fundamental questions that could not be tackled by bulk-level ...

When normalizing data structures, attributes congregate around the business keys that identify the grain at which those attributes derive their values. Attributes directly related to a person, ...

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 ...

This article explains how to programmatically normalize numeric data for use in a machine learning (ML) system such as a deep neural network classifier or clustering algorithm. Suppose you are trying ...

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 ...

Improve model performance and training stability using multilayer perceptrons (MLPs) and applying normalization techniques. Implement autoencoders for unsupervised feature learning and design ...