June 8, 2017  |  Cytobank, DROP, Education

Maximize Your Insight with Machine Learning Algorithms
and NanoString® 3D Biology™ Technology

Courtesy: NanoString
Image courtesy: NanoString

Scientists across many therapeutic areas are striving to solve complex biological problems by measuring multiple analytes, thinking that together these data will power deeper discovery. However, analyzing these data independently is less effective and more time-consuming than analyzing them together. Cytobank’s new DROP feature allows scientists to apply machine learning algorithms to many data types, including these datasets, and to develop integrated insights quickly.

With bulk data, unsupervised machine learning algorithms on Cytobank can help you identify clinically relevant groups of samples by combining information from all of your markers at the same time. We’ll illustrate that here with Nanostring® 3D Biology technology, which simultaneously analyzes up to 800 SNVs, RNA, and proteins and phospho-proteins from the same sample. In this example, the assays used profile 104 SNV and small InDels, 192 RNA, and 28 total and phospho-proteins in 144 samples.

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March 14, 2017  |  CITRUS, Education

Getting the Most out of CITRUS Part I: Imagine What You Can Do

This CITRUSsnapshotis Part I of a blog series where Cytobank’s Director of Informatics, Katherine Drake, PhD, will discuss important aspects of using CITRUS to your full advantage. She’ll help you understand when you should use CITRUS, what makes it so powerful, and what you need to know to choose the best setup options for your data.

Since we implemented CITRUS in Cytobank last fall, many scientists have taken advantage of its automated biomarker discovery pipeline to answer questions and find predictive models across a wide range of diseases. Here, I’ll discuss its capabilities and review some examples to help you imagine how you can use CITRUS, too. More »