Investigation of different features for predicting ionization efficiency of metabolites in mass spectrometry using Gaussian processes

The field of metabolomics studies the role of small molecules called metabolites. Mass spectrometry (MS) is widely used for identification and quantitation of metabolites. Extracting knowledge from MS data is a difficult problem and a lot of research is done in the area of identification of metabolites. However, precise quantitation of the identified metabolites is still challenging. This dissertation investigates the use of Gaussian process regression for probabilistic correction of MS signal intensities based on physico-chemical properties and molecule structure of the studied metabolites. Education Use Consent I hereby give my permission for this project to be shown to other University of Glasgow students and to be distributed in an electronic format. Please note that you are under no obligation to sign this declaration, but doing so would help future students.