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
- Find an optimal gradient for your LC-MS/MS analysis within 10 runs.
- Wonder why BAGO is efficient? Read more about acquisition functions.
Omics-scale evaluation on compound separation
- Separation efficiency was defined to evaluate the performance of a gradient.
- Wonder how omics-scale evaluation is achieved? Read more about encodings.
Broader discovery of chemical space
- Expand your discovery of chemical space by improving identification and quantification.
- Wonder how BAGO can help you? Read more about applications.
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:
- Perform LC gradient optimization in programmtic envrionment
- Model LC-MS experiment to evaluate compound separation performance
- Optimize the default pipeline to adpat a special gradient optimization scenario
- Further development of the proposed Bayesian optimization strategy
- Extend the corrent stategy to other LC-based analytical platforms
- Documentation: https://bago.readthedocs.io/en/latest/
- Source code: https://github.com/huaxuyu/bago
- Bug reports: https://github.com/huaxuyu/bago/issues
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.