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What is aRT?
What is aRT?
The Automated Recommendation Tool (ART) leverages machine learning to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system.
ART is a supervised learning algorithm that combines machine learning, Bayesian inference, and Monte Carlo sampling to guide very effective active learning processes. While developed to meet synthetic biology’s special needs, it is a general algorithm that can be easily applied to other problems with similar characteristics: small data sets, the need for uncertainty quantification, and recursive cycles.
Media coverage
Media coverage
Automated Recommendation Tool (ART): Machine Learning for Synthetic Biology (UC Berkeley Deep Tech Innovation Lab)
Machine Learning Takes on Synthetic Biology: Algorithms Can Bioengineer Cells for You (Berkeley Lab News, phys.org, LBL BioSciences, MirageNews, Bioengineer.org, Sciencesprings, Scienceblog)
Machine Learning Takes On Synthetic Biology: Revolutionary Algorithms Can Rapidly Bioengineer Cells for You (SciTechDaily)
Speeding Up the Ability to Design New Biological Systems (Lab Manager)
New algorithm to improve the efficiency of synthetic biology experiments (Innovation News Network)
Algorithm suggests optimal designs for new biological systems (The E&T)
A revolution in bioengineering (Science Node)
Machine Learning Meets Synthetic Biology (Medium)
How Machine Learning Made Hops-Free Hoppy Beer (and Other SynBio Wonders) Possible (Singularity Hub)
New AI Speeds Discovery in Synthetic Biology (Psychology Today)
Berkeley Lab scientists design automated synthetic biology tool (The Daily Californian)
publications
publications
TEAM
TEAM
Tijana Radivojevic
Hector Garcia Martin
Zak Costello
Mark Forrer
Kenneth Workman
Bret Peterson
Nathan Hillson
Robin Johnston
Aparajitha Srinivasan
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