Machine learning techniques of de-novo molecular formula reconstruction from its mass spectrum – Bc. Adam Hájek
Bc. Adam Hájek
Master's thesis
Machine learning techniques of de-novo molecular formula reconstruction from its mass spectrum
Machine learning techniques of de-novo molecular formula reconstruction from its mass spectrum
Abstract:
Hmotnostní spektrometrie je metoda analytické chemie, která analyzuje molekuly pomocí jejich fragmentace a následného změření hmotnosti vzniklých fragmentů. Tato diplomová práce se zabývá úkolem rekonstrukce molekulární struktury z GC-EI-MS spekter a to metodou de novo. Tato metoda se zaměřuje na případy, kdy analyzovaná molekula dosud nebyla pomocí spektometru změřena a není tudíž součástí žádné referenční …moreAbstract:
Mass spectrometry is an analytical chemistry technique that analyzes molecules by fragmenting them and measuring the weights of these fragments. This diploma thesis addresses the task of molecular structure elucidation from GC-EI-MS spectra de novo, meaning we do not have a reference spectrum of this molecule from a different experiment. The task aims to reverse the process of molecular fragmentation …more
Language used: English
Date on which the thesis was submitted / produced: 21. 5. 2024
Identifier:
https://is.muni.cz/th/vao1s/
Thesis defence
- Date of defence: 19. 6. 2024
- Supervisor: Mgr. Aleš Křenek, Ph.D.
- Reader: doc. Mgr. Bc. Vít Nováček, PhD
Citation record
ISO 690-compliant citation record:
HÁJEK, Adam. \textit{Machine learning techniques of de-novo molecular formula reconstruction from its mass spectrum}. Online. Master's thesis. Brno: Masaryk University, Faculty of Informatics. 2024. Available from: https://theses.cz/id/2h5vcl/.
Full text of thesis
Contents of on-line thesis archive
Published in Theses:- světu
Other ways of accessing the text
Institution archiving the thesis and making it accessible: Masarykova univerzita, Fakulta informatikyMasaryk University
Faculty of InformaticsMaster programme / field:
Artificial intelligence and data processing / Machine learning and artificial intelligence
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