We have assessed the utility of RNA titration samples for evaluating microarray platform performance and the impact of different normalization methods on the results obtained. As part of the ...

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 ...

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 ...

What is data cleaning in machine learning? Data cleaning in machine learning (ML) is an indispensable process that significantly influences the accuracy and reliability of predictive models. It ...

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 ...

See a spike in your DNA–protein interaction quantification results with these guidelines for spike-in normalization. A team of researchers at the University of California San Diego (CA, USA) have ...

Data rarely comes in usable form. Data wrangling and exploratory data analysis are the difference between a good data science model and garbage in, garbage out. Novice data scientists sometimes have ...

Compared to other clustering techniques, DBSCAN does not require you to explicitly specify how many data clusters to use, explains Dr. James McCaffrey of Microsoft Research in this full-code, ...

In today's data-driven economy, the ability to effectively manage, integrate, and leverage vast amounts of information is paramount to business success. Yet, many organizations find themselves ...

Alexandra Twin has 15+ years of experience as an editor and writer, covering financial news for public and private companies. Natalya Yashina is a CPA, DASM with over 12 years of experience in ...