How Flow Cytometry Takes Advantage of Machine Learning

View this email as a web page here.

Algorithms for Flow Cytometry

Coupling Machine Learning

and Flow Cytometry

Research continues to highlight the biological importance of cellular heterogeneity, unveiling novel cell types and subtypes with physiological and pathological relevance. Modern flow cytometry analyzes dozens of parameters simultaneously, resulting in millions of possible outcome combinations in a given experiment.

Researchers typically cope with large data volumes by focusing their observations on specific parameters. To do this, they execute manual gating; however, this can miss key relationships and connections because it is imprecise and it does not parse the entire dataset. Machine learning can bridge this information gap.

Download this poster from Beckman Coulter to discover how machine learning—both automated gating and algorithmic data analysis—helps researchers take full advantage of modern flow cytometry.



Never miss an email from The Scientist.
Click here to add The Scientist to your safe-sender list.

The Scientist, 1000 N West Street, Suite 1200, 
Wilmington, Delaware, United States, 19801  
Toll-Free: (888) 781-0328 | Phone: (705) 528-6888

You received this email because you are subscribed to The Scientist Special Offers and Promotions from The Scientist.
Click to Unsubscribe | View Privacy Policy

Stop receiving email from The Scientist


Posts les plus consultés de ce blog

Chris Ramsey can take the heat, but what would relegation for QPR mean for black managers in the Premier League?

How a team of innovators overcame the odds to create water from thin air

Britain's health service uses long Twitter thread to explain why it needs more black people to donate blood