Nov 8, 2018

Maximizing the quality of NMR automatic metabolite profiling by a machine learning based prediction of signal parameters

BioRxiv : the Preprint Server for Biology
Daniel CanuetoNicolau Canellas


The quality of automatic metabolite profiling in NMR datasets in complex matrices can be compromised by the multiple sources of variability in the samples. These sources cause uncertainty in the metabolite signal parameters and the presence of multiple low-intensity signals. Lineshape fitting approaches might produce suboptimal resolutions or distort the fitted signals to adapt them to the complex spectrum lineshape. As a result, tools tend to restrict their use to specific matrices and strict protocols to reduce this uncertainty. However, the analysis and modelling of the signal parameters collected during a first profiling iteration can further reduce the uncertainty by the generation of narrow and accurate predictions of the expected signal parameters. In this study, we show that, thanks to the predictions generated, better profiling quality indicators can be outputted and the performance of automatic profiling can be maximized. Thanks to the ability of our workflow to learn and model the sample properties, restrictions in the matrix or protocol and limitations of lineshape fitting approaches can be overcome.

  • References
  • Citations


  • We're still populating references for this paper, please check back later.
  • References
  • Citations


  • This paper may not have been cited yet.

Mentioned in this Paper

Extracellular Matrix
Profile (Lab Procedure)
Spectroscopy, Nuclear Magnetic Resonance
Adapt (substance)
Human Activity Profile Test

About this Paper

Related Feeds

BioRxiv & MedRxiv Preprints

BioRxiv and MedRxiv are the preprint servers for biology and health sciences respectively, operated by Cold Spring Harbor Laboratory. Here are the latest preprint articles (which are not peer-reviewed) from BioRxiv and MedRxiv.

Related Papers

California State Journal of Medicine
Metabolomics : Official Journal of the Metabolomic Society
Daniel CanuetoNicolau Canellas
Ugeskrift for laeger
Stefan Walbom Harders
Magnetic Resonance in Chemistry : MRC
D A CheshkovV A Chertkov
© 2020 Meta ULC. All rights reserved