Asset Details
Do you wish to reserve the book?
PeakPerformance - A software tool for fitting LC-MS/MS peaks including uncertainty quantification by Bayesian inference
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
PeakPerformance - A software tool for fitting LC-MS/MS peaks including uncertainty quantification by Bayesian inference
Do you wish to request the book?
PeakPerformance - A software tool for fitting LC-MS/MS peaks including uncertainty quantification by Bayesian inference
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Paper
PeakPerformance - A software tool for fitting LC-MS/MS peaks including uncertainty quantification by Bayesian inference
2024
Overview
A major bottleneck of chromatography-based analytics has been the elusive fully automated identification and integration of peak data without the need of extensive human supervision. The presented Python package PeakPerformance applies Bayesian inference to chromatographic peak fitting, and provides an automated approach featuring model selection and uncertainty quantification. Currently, its application is focused on data from targeted liquid chromatography tandem mass spectrometry (LC-MS/MS), but its design allows for an expansion to other chromatographic techniques. Availability and implementation: PeakPerformance is implemented in Python and the source code is available on https://github.com/JuBiotech/peak-performance. It is unit-tested on Linux and Windows and accompanied by general introductory documentation, as well as example notebooks. Contact: s.noack@fz-juelich.deCompeting Interest StatementThe authors have declared no competing interest.
Publisher
Cold Spring Harbor Laboratory Press
Subject
