Riffyn Makes Machine Learning in Science an Everyday Reality

Riffyn Team

Poorly designed studies, unqualified assays, and bad data systems lead to undersampled, noisy, unannotated or under-annotated datasets, and datasets that simply aren’t structured appropriately for statistical analyses. This pretty much guarantees poor results when we try to apply machine learning. To make machine learning in science an everyday reality, we need better designed assays that produce higher quality, more usable data. We need to reshape the entire scientific experiment with scientific blueprints.

Read how Riffyn accomplishes this in the SynBioBeta interview with Timothy Gardner, Founder and CEO, Loren Perelman, Vice President of Science, and John F. Conway, Chief Commercial Officer.