What is Machine Learning?

If you’re confused about the differences between machine learning, AI, deep learning, supervised learning, unsupervised learning, etc., you’re not alone. Tim Gardner explains the differences between all these terms, and reveals that you may have even been doing machine learning for years and not even known it.

Read More
Timothy Gardner
Riffyn Introduces its Tableau Web Data Connector

Given the tremendous popularity of Tableau as a data visualization and analysis tool, we are super excited to introduce the Riffyn Web Data Connector (WDC) for Tableau users! It allows Riffyn users to securely transfer Riffyn-generated data tables directly into Tableau for immediate analysis and visualization. All you need is a web connection. No need to mess with files.

Read More
Douglas Williams
The Importance of Measurement Systems

Your science is only as good as your ability to measure outcomes. Where should effort be placed in measurement system development? Here we outline the key drivers that make your measurement capability work for you and enable maximum throughput without sacrificing data quality or increasing costs needlessly.

Read More
Loren Perelman
Process Models are Leading the Way in Bioprocess Development

The BioProcess International West conference, held in San Francisco CA from March 19-22 2018, reinforced the state of the bioprocess industry for biotech companies looking to accelerate and intensify their manufacturing processes. The most consistent theme that emerged was the importance of establishing data-driven process models across each of the functional areas within process development. Here we discuss ways companies are taking action towards their goals.

Read More
Douglas Williams
False Discovery Rate: The Most Important Scientific Calculation You Never Do

As a full-time scientist you test a lot of hypothesis—a lot more than one per week. Every sample or condition you compare in an experiment is a hypothesis test. If you test tens or hundreds or even thousands of samples in a single experiment, it’s guaranteed that you’ll find many false positive results, in every experiment. So what can you do about it? You can calculate your False Discovery Rate (FDR), which tells you what fraction of your accepted tests are false.

Read More
Timothy Gardner Comment
Understanding False Discovery Rate

FDR is a very simple concept. It is the number of false discoveries in an experiment divided by total number of discoveries in that experiment. But there is a problem, you never know how many discoveries are actually real or false when you accepted them. So how do you estimate FDR from your data?

Read More
Timothy GardnerComment
Understanding Family-Wise Error Rate

Family-Wise Error Rate (FWER) is an approach for multiple testing correction. It is a scary sounding term, but don’t be deterred. It’s simply the probability that one or more of your “family” of multiple tests is false. In my view, FDR is generally more aligned with what you want as an experimenter. But FWER offers an alternative, generally more stringent, approach to reduce your false findings.  

Read More
Timothy GardnerComment
Buried in Data and Starving for Information

Since the time of Newton and Galileo, the tools for capturing and communicating science have remained conceptually unchanged. But these tools are wholly inadequate for the complexity of today’s scientific challenges. Riffyn created the Scientific Development Environment to change that.

Read More
Timothy Gardner