Creation date in repository: 2023-02-10

Abstract: Disease diagnosis and personalized medicine based on metabolomics using changes in metabolite concentrations, are attracting the attention of more and more researchers. Nevertheless, compound identification remains a problem in most metabolomics studies based on mass spectrometry (MS), as the percentage of known MS molecular spectra is very low. We have proposed different methodologies to obtain the best prediction of the tandem mass spectra of molecules, comparing different types of neural networks. Also, we have obtained a very good prediction ability, achieving better results than the best in silico tool for the prediction of MS/MS spectra up to date.

Subject: Metabolòmica

Language: en

Work's codirector: Guimerà Manrique, Roger

Subject areas: Ingeniería química Chemical engineering Enginyeria química

Department: Enginyeria Química

Student: Pérez Ribera, María Isabel

Academic year: 2021-2022

Title in different languages: Predicción computacional de espectros de masas moleculares en tándem mediante algoritmos de aprendizaje profundo Computational prediction of molecular tandem mass spectra using deep learning algorithms Predicció computacional d'espectres de masses moleculars en tàndem per mitjà d'algoritmes d'aprenentatge profund

Work's public defense date: 2022-06-30

Access rights: info:eu-repo/semantics/openAccess

Keywords: redes neuronales, metabolómica, espectrometría de masas neural networks, metabolomics, mass spectrometry xarxes neuronals, metabolòmica, espectrometria de masses

Confidenciality: No

Title in original language: Computational prediction of molecular tandem mass spectra using deep learning algorithms

Project director: Sales Pardo, Marta

Education area(s): Enginyeria Biomèdica

Entity: Universitat Rovira i Virgili (URV)