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master's thesis
Poboljšanje sastavljenih genoma metodama dubokog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. urn:nbn:hr:168:912275
Jurić, Antonio
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Jurić, A. (2018). Poboljšanje sastavljenih genoma metodama dubokog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:912275

Jurić, Antonio. "Poboljšanje sastavljenih genoma metodama dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. https://urn.nsk.hr/urn:nbn:hr:168:912275

Jurić, Antonio. "Poboljšanje sastavljenih genoma metodama dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. https://urn.nsk.hr/urn:nbn:hr:168:912275

Jurić, A. (2018). 'Poboljšanje sastavljenih genoma metodama dubokog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 24 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:912275

Jurić A. Poboljšanje sastavljenih genoma metodama dubokog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2018 [cited 2024 December 24] Available at: https://urn.nsk.hr/urn:nbn:hr:168:912275

A. Jurić, "Poboljšanje sastavljenih genoma metodama dubokog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2018. Available at: https://urn.nsk.hr/urn:nbn:hr:168:912275

Metadata
TitlePoboljšanje sastavljenih genoma metodama dubokog učenja
Title (english)Assembly Improvement Using Deep Learning Methods
AuthorAntonio Jurić
MentorMile Šikić (mentor)
Committee memberMile Šikić (predsjednik povjerenstva)
Committee memberMarko Subašić (član povjerenstva)
Committee memberKrešimir Križanović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2018-09-20, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Razvijanje alata koji sastavljaju genom s dovoljnom točnošću da bi bili upotrebljivi u praksi još je otvoren problem. Uređaji treće generacije za sekvenciranje genoma omogućuju sastavljanje genoma s manjom stopom fragmentiranosti te visokom točnošću. Konačnu točnost sastavljenog genoma u ovom radu pokušavamo popraviti metodama dubokog učenja. Trenirana konvolucijska duboka mreža na temelju naučenih frekvencija pojedinih baza, umetanja i brisanja popravlja konačni genom ispravljajući pronađene pogreške. Mreže su pripremljene za Pacific Biosciences te Oxford Nanopore podatke. Trenutni rezultati pokazuju da modeli na nekim konsenzusima neznatno poprave, dok na drugima neznatno snize druge točnost. No, modele je moguće još popraviti s nizom neisprobanih ideja opisanih na kraju rada.
Abstract (english)
Development of tools for genome assembling with enough precision to be useful in practice is still an open problem. Third generation sequencers allow genome assembly with smaller fragmentation and high accuracy. In this work, we try to improve the final accuracy of the assembled genome using deep learning techniques. Trained convolutional deep network polishes errors in the assembled genome by learning the correct bases, insertions and deletions patterns. Networks are prepared to be used with Pacific Biosciences and Oxford Nanopore data. Current results show that sometimes models slightly improve, sometimes slightly degrade consensus quality, but there is still a room for progress since there are a lot of untested ideas which are described in the end of the thesis.
Keywords
Keywords (english)
Languageenglish
URN:NBNurn:nbn:hr:168:912275
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 on2019-03-13 21:19:50