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master's thesis
Određivanje podtipova virusa korištenjem modela dubokog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:657047
Martinović, Jana
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Martinović, J. (2023). Određivanje podtipova virusa korištenjem modela dubokog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:657047

Martinović, Jana. "Određivanje podtipova virusa korištenjem modela dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:657047

Martinović, Jana. "Određivanje podtipova virusa korištenjem modela dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:657047

Martinović, J. (2023). 'Određivanje podtipova virusa korištenjem modela dubokog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 22 July 2024, https://urn.nsk.hr/urn:nbn:hr:168:657047

Martinović J. Određivanje podtipova virusa korištenjem modela dubokog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2024 July 22] Available at: https://urn.nsk.hr/urn:nbn:hr:168:657047

J. Martinović, "Određivanje podtipova virusa korištenjem modela dubokog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:657047

Metadata
TitleOdređivanje podtipova virusa korištenjem modela dubokog učenja
Title (english)Deep learning-based detection of viral subtypes
AuthorJana Martinović
MentorMirjana Domazet-Lošo (mentor)
Committee memberMirjana Domazet-Lošo (predsjednik povjerenstva)
Committee memberTomislav Jagušt (član povjerenstva)
Committee memberDanijel Mlinarić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2023-07-03, Croatia
Abstract
Rješavanje problema detekcije podtipova virusa omogućilo bi lakšu analizu genomskih sljedova virusa, kao i pronalazak epidemioloških i geografskih poveznica između pacijenata te bi potencijalno olakšalo osmišljavanje terapije prilagođene za infekciju pojedinim podtipom. Trenutno se detekcija podtipova virusa provodi algoritmima strojnog učenja poput skrivenih Markovljevih lanaca ili raznih algoritama grupiranja. U ovom radu iskorištena je arhitektura dubokog učenja transformer, koja se pokazala iznimno uspješnom u području obrade prirodnog jezika. Arhitektura transformer je proširena na način da neovisno trenira pojedine podnizove ulaznoga slijeda, što dodatno omogućuje i detekciju rekombinanata virusa. Dobiveni rezultati se nisu pokazali boljim od već postojećih rješenja, ali demonstriraju velik potencijal primjene transformera za rješavanje ovog problema.
Abstract (english)
Solving the problem of virus subtype detection would enable easier analysis of viral genomic sequences, as well as the discovery of epidemiological and geographical links between patients. It also has the potential to develop therapies for specific subtype infections easier. Currently, virus subtype detection is performed using machine learning algorithms such as hidden Markov models or various clustering algorithms. In this study, the transformer architecture, which has shown exceptional success in the field of natural language processing, was used. The transformer architecture was extended to independently train individual subsequences of the input sequence, which enables the detection of viral recombinants. The obtained results were not better than the existing solutions, but they demonstrate the great potential for solving this problem.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:657047
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-07-13 10:20:16