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master's thesis
Model dubokog učenja pore za sekvenciranje nanoporama
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:713036
Penić, Rafael Josip
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Penić, R. J. (2021). Model dubokog učenja pore za sekvenciranje nanoporama (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:713036

Penić, Rafael Josip. "Model dubokog učenja pore za sekvenciranje nanoporama." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:713036

Penić, Rafael Josip. "Model dubokog učenja pore za sekvenciranje nanoporama." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:713036

Penić, R. J. (2021). 'Model dubokog učenja pore za sekvenciranje nanoporama', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 11 July 2024, https://urn.nsk.hr/urn:nbn:hr:168:713036

Penić RJ. Model dubokog učenja pore za sekvenciranje nanoporama [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2024 July 11] Available at: https://urn.nsk.hr/urn:nbn:hr:168:713036

R. J. Penić, "Model dubokog učenja pore za sekvenciranje nanoporama", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:713036

Metadata
TitleModel dubokog učenja pore za sekvenciranje nanoporama
Title (english)Deep Learning Model of Nanopore Sequencing Pore
AuthorRafael Josip Penić
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 country2021-07-01, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Sekvenciranje nanoporom je jedna od najmodernijih metoda sekvenciranja bioloških sekvenci i u ovom radu smo istražili kako samo-nadzirajuće metode utječu na performanse modela zaduženih za određivanje baza i slične zadatke. Isprobali smo metodu kontrastivnog prediktivnog kodiranja kojom smo pokušali predvidjeti buduće latentne reprezentacije signala. Nažalost, ovaj pristup nije uspio unaprijediti točnost modela. Najbolje rezultate smo dobili metodama učenja kvalitetnih reprezentacija signala koje su iznimno popularne u području računalnog vida. Ključnu ulogu kod ovakih algoritama imaju transformacije signala i vrlo je važno smisliti dobre transformacije koje će pomoći modelu da nauči kvalitetne vektorske reprezentacije.
Abstract (english)
Nanopore sequencing is one of the most modern sequencing technologies and in this thesis we explored how do the self-supervised methods affect models that work with nanopore readings. We tried out contrastive predictive coding method with which we tried to predict signal's future latent representations. Sadly, this approach did not improve model's performance on the downstream task. We achieved best results with self-supervised representation learning methods which are very popular in the field of computer vision. Signal augmentations have a very important role in these algorithms and it is very important to think of good augmentations that will help model learn high-quality signal representations.
Keywords
Keywords (english)
Languageenglish
URN:NBNurn:nbn:hr:168:713036
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 on2023-01-13 13:27:24