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undergraduate thesis
Poliranje DNA slijeda koristeći metode dubokog učenja
2019. urn:nbn:hr:168:755208
Vrček, Lovro
University of Zagreb
Faculty of Electrical Engineering and Computing

Cite this document

Vrček, L. (2019). Poliranje DNA slijeda koristeći metode dubokog učenja (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:755208

Vrček, Lovro. "Poliranje DNA slijeda koristeći metode dubokog učenja." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:755208

Vrček, Lovro. "Poliranje DNA slijeda koristeći metode dubokog učenja." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:755208

Vrček, L. (2019). 'Poliranje DNA slijeda koristeći metode dubokog učenja', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 06 April 2025, https://urn.nsk.hr/urn:nbn:hr:168:755208

Vrček L. Poliranje DNA slijeda koristeći metode dubokog učenja [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2019 [cited 2025 April 06] Available at: https://urn.nsk.hr/urn:nbn:hr:168:755208

L. Vrček, "Poliranje DNA slijeda koristeći metode dubokog učenja", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:168:755208

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Metadata
TitlePoliranje DNA slijeda koristeći metode dubokog učenja
Title (english)DNA Sequence Polishing Using Deep learning
AuthorLovro Vrček
MentorMile Šikić (mentor)
Committee memberMile Šikić (predsjednik povjerenstva)
Committee memberKrešimir Križanović (član povjerenstva)
Committee memberJosip Marić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2019-07-16, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Cilj ovog rada bio je implementirati model temeljen na dubokom uˇcenju koji bi pove´cao toˇcnost sastavljenog genoma. Konstruirani model temelji se na arhitekturi rezidualne neuronske mreže te postiže toˇcnost ve´cu od trenutno najboljih alata temeljenih na klasiˇcnom raˇcunarstvu, no zaostaje ne nadmašuje najbolje alate temeljene na dubokom ucˇenju. Med¯utim, zbog ogranicˇenja u vidu prostora za pohranu podataka i vremena treniranja modela, detaljnija analiza tek treba biti napravljena za više... More bakterijskih uzoraka i razliˇcite arhitekture mreže. Less
Abstract (english)
Goal of this work was to implement a deep learning model in order to increase the accuracy of the assembled genome. This model is based on a residual network architecture and achieves higher accuracy than classical state-of-the-art tools, but falls behind the best deep-learning tools available. However, due to limitations in terms of storage and time for training the model, further analysis has to be conducted for different bacteria datasets and network architectures.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:755208
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsClosed access
Terms of useLicence In copyright icon
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Created on2020-01-07 22:57:56
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