prikaz prve stranice dokumenta Detekcija varijacija u jednom nukleotidu uporabom metoda dubokog učenja
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master's thesis
Detekcija varijacija u jednom nukleotidu uporabom metoda dubokog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. urn:nbn:hr:168:790213
Stanojević, Dominik
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Stanojević, D. (2019). Detekcija varijacija u jednom nukleotidu uporabom metoda dubokog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:790213

Stanojević, Dominik. "Detekcija varijacija u jednom nukleotidu uporabom metoda dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:790213

Stanojević, Dominik. "Detekcija varijacija u jednom nukleotidu uporabom metoda dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:790213

Stanojević, D. (2019). 'Detekcija varijacija u jednom nukleotidu uporabom metoda dubokog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 26 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:790213

Stanojević D. Detekcija varijacija u jednom nukleotidu uporabom metoda dubokog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2019 [cited 2024 December 26] Available at: https://urn.nsk.hr/urn:nbn:hr:168:790213

D. Stanojević, "Detekcija varijacija u jednom nukleotidu uporabom metoda dubokog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:168:790213

Metadata
TitleDetekcija varijacija u jednom nukleotidu uporabom metoda dubokog učenja
Title (english)Detection of Single Nucleotide Variations Using Deep Learning
AuthorDominik Stanojević
MentorBojana Dalbelo-Bašić (mentor)
Committee memberBojana Dalbelo-Bašić (predsjednik povjerenstva)
Committee memberKrešimir Križanović (član povjerenstva)
Committee memberMarko Čupić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2019-07-10, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Detekcija varijacija je veoma važan problem u bioinformatics zbog mnogo razloga. Osim njegove važnosti, problem detekcije varijacija je zanimljiv zbog teške izgradnje efikasnog i preciznog modela. U ovom radu dan je teoretski uvod u problem detekcije varijacija, s posebnim naglaskom na jednonukleotidne varijacije u diploidnim organizmima. U radu su opisani korišteni podaci kao i postupak izgradnje skupa za učenje i testiranje. Također, u radu je opisan prilagođen model ResNet specifično izgrađen za postupak detekcije varijacija. Dani model ostvaruje rezultate usporedive s drugim modelima koristeći manji skup podataka od drugih modela, primjerice modela Clairvoyante.
Abstract (english)
Variant calling, the task of finding variants is extremely important in bioinformatics for various reasons. Expect being important, it is difficult to build efficient and accurate variant caller. This thesis gives theoretical introduction to the variant calling problem, focusing on single nucleotide variants in diploid organisms. The data and dataset generation are described and its application to the custom ResNet model built for variant calling. The model achieves comparable results using only a fraction of data compared to Clairvoyante for predicting single nucleotide variants.
Keywords
Keywords (english)
Languageenglish
URN:NBNurn:nbn:hr:168:790213
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 on2020-01-07 22:07:04