Asynchronous parallel Bayesian optimization for AI-driven cloud laboratories.

Journal: Bioinformatics (Oxford, England)
Published Date: Jul 12, 2021

Abstract

MOTIVATION: The recent emergence of cloud laboratories-collections of automated wet-lab instruments that are accessed remotely, presents new opportunities to apply Artificial Intelligence and Machine Learning in scientific research. Among these is the challenge of automating the process of optimizing experimental protocols to maximize data quality.

Authors

  • Trevor S Frisby
    Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA.
  • Zhiyun Gong
    Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
  • Christopher James Langmead
    Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

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