RNA-Seq and gene expression microarrays provide comprehensive profiles of gene activity, but lack of reproducibility has hindered their application. A key challenge in the data analysis is the ...

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 ...

When the healthcare industry talks about data, the conversation usually focuses on interoperability and data standards. These are certainly important topics, but they don’t fully address the challenge ...

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 ...

GenEx System Aids in the Selection of Reference Genes for Standardizing mRNA Measurements Quantitative real-time PCR (qPCR) is currently the prime technique to measure gene expression. When properly ...

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 ...

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, ...