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master's thesis
Primjena dubokog učenja u genetici
Rijeka: University of Rijeka, Faculty of Engineering, 2024. urn:nbn:hr:190:829160
Županović, Luka
University of Rijeka
Faculty of Engineering
Department of Computer Engineering
Section of Software Engineering

Institutional repository: Repository of the University of Rijeka, Faculty of Engineering

Cite this document

Županović, L. (2024). Primjena dubokog učenja u genetici (Master's thesis). Rijeka: University of Rijeka, Faculty of Engineering. Retrieved from https://urn.nsk.hr/urn:nbn:hr:190:829160

Županović, Luka. "Primjena dubokog učenja u genetici." Master's thesis, University of Rijeka, Faculty of Engineering, 2024. https://urn.nsk.hr/urn:nbn:hr:190:829160

Županović, Luka. "Primjena dubokog učenja u genetici." Master's thesis, University of Rijeka, Faculty of Engineering, 2024. https://urn.nsk.hr/urn:nbn:hr:190:829160

Županović, L. (2024). 'Primjena dubokog učenja u genetici', Master's thesis, University of Rijeka, Faculty of Engineering, accessed 11 January 2025, https://urn.nsk.hr/urn:nbn:hr:190:829160

Županović L. Primjena dubokog učenja u genetici [Master's thesis]. Rijeka: University of Rijeka, Faculty of Engineering; 2024 [cited 2025 January 11] Available at: https://urn.nsk.hr/urn:nbn:hr:190:829160

L. Županović, "Primjena dubokog učenja u genetici", Master's thesis, University of Rijeka, Faculty of Engineering, Rijeka, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:190:829160

Metadata
TitlePrimjena dubokog učenja u genetici
Title (english)Applications of deep learning in genetics
AuthorLuka Županović
MentorGoran Mauša (mentor)
Committee memberSandi Ljubić (predsjednik povjerenstva)
Committee memberZoran Čarija (član povjerenstva)
Committee memberGoran Mauša (član povjerenstva)
GranterUniversity of Rijeka
Faculty of Engineering
(Department of Computer Engineering)
(Section of Software Engineering)
Rijeka
Defense date and country2024-11-25, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Program Engineering
Abstract
Kao jedan od problema u domeni genomike u kojem je strojno učenje često primijenjeno jest klasifikacija bolesti temeljena na podacima genske ekspresije. Ovaj diplomski rad bavi se predviđanjem raka uz pomoć takvih podataka. Na samom početku rada čitatelj je uveden u tematiku te je objašnjena biološka pozadina potrebna za razumijevanje ovog rada. Nadalje, istraženi su i navedeni javno dostupni resursi podataka genske ekspresije. Kao praktični dio ovoga rada razvijena su i optimizirana tri klasifikacijska modela dubokog učenja korištenjem PyTorch knjižnice. Pri kraju rada izvršena je detaljna analiza performansi modela.
Abstract (english)
One of the key challenges in the field of genomics, where machine learning is frequently ap- plied, is disease classification given the gene expression data. This thesis aims to predict the recurrence of breast cancer using such data. The work begins by introducing the reader to the subject, providing the biological background necessary to understand the scope of this study. Furthermore, publicly available gene expression data resources are explored and listed. As the practical component of this thesis, three deep learning classification models were developed and optimized using the PyTorch library. Toward the end of this thesis, a detailed performance analysis of the models is conducted.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:190:829160
ProjectNumber: UNIRI-INOVA-3-23-1
Title: Primjena umjetne inteligencije u predviđanju genskog izražaja
Title: Artificial Intelligence for Gene Expression Prediction
Acronym: ANTIGEN
Leader: Goran Mauša
Jurisdiction: Croatia
Funder: Sveučilište u Rijeci
Study programmeTitle: Computer Engineering
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
Access conditionsOpen access
Terms of useLicence Attribution 4.0 International (CC BY 4.0) icon
Created on2024-11-29 13:59:01