Fully automated LC gradient optimization of optimal compound separation in nontargeted metabolomics
BAGO is a Bayesian optimization strategy for LC gradient optimization for MS-based small molecule analysis. Check out our YouTube video
Highly efficient gradient optimization
Omics-scale evaluation on compound separation
Broader discovery of chemical space
BAGO Windows software is freely available from the GitHub release page. A user manual in .pdf format is included with the software.
The software is designed to be simple, clear, and intuitive. BAGO has a graphical user interface as shown below.
bago is a Python package for supporting the proposed Bayesian Optimization framework of LC gradient optimization.
bago covers the proposed features needed in creating a gradient optimization workflow based on Bayesian optimization. Depending on your use case, bago can be used in different ways:
Yu, H., Biswas, P., Rideout, E., Cao, Y., & Huan, T. (2023). Bayesian optimization of separation gradients to maximize the performance of untargeted LC-MS. bioRxiv, 2023-09.
FAQs
Fully automated LC gradient optimization of optimal compound separation in nontargeted metabolomics
We found that bago demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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