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master's thesis
Prepoznavanje pojačivača u genomu metodama strojnog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. urn:nbn:hr:168:950014
Milisavljević, Alexandra
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Milisavljević, A. (2022). Prepoznavanje pojačivača u genomu metodama strojnog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:950014

Milisavljević, Alexandra. "Prepoznavanje pojačivača u genomu metodama strojnog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:950014

Milisavljević, Alexandra. "Prepoznavanje pojačivača u genomu metodama strojnog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:950014

Milisavljević, A. (2022). 'Prepoznavanje pojačivača u genomu metodama strojnog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 27 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:950014

Milisavljević A. Prepoznavanje pojačivača u genomu metodama strojnog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 December 27] Available at: https://urn.nsk.hr/urn:nbn:hr:168:950014

A. Milisavljević, "Prepoznavanje pojačivača u genomu metodama strojnog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:950014

Metadata
TitlePrepoznavanje pojačivača u genomu metodama strojnog učenja
Title (english)Application of machine learning methods to genome-wide prediction of enhancers
AuthorAlexandra Milisavljević
MentorMirjana Domazet-Lošo (mentor)
Committee memberMirjana Domazet-Lošo (predsjednik povjerenstva)
Committee memberTomislav Jagušt (član povjerenstva)
Committee memberMarko Horvat (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-06-30, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Pojačivači u genomu su cis-regulatorni elementi DNA koji pozitivno reguliraju transkripciju određenih gena. Predviđa se da je u ljudskom genomu na stotine tisuća pojačivača, što zahtijeva previše resursa i vremena da bi se provjerilo eksperimentalno. Primjenom metoda strojnog učenja mogli bi se dobiti modeli koji sa zadovoljavajućom preciznošću klasificiraju je li sekvenca DNA pojačivač ili ne. U ovom radu promatra se tri grupe metoda strojnog učenja: metoda potpornih vektora (engl. support vector machine SVM), neuronska mreža MLP (engl. multilayer perceptron) i ensemble učenje. Rezultati dobivenih modela se uspoređuju s dva postojeća alata iEnhancer-2L i iEnhancer-EL. Moguća je vizualizacija rezultata predikcije više modela za istu DNA sekvencu.
Abstract (english)
Enhancers are cis-regulatory elements of DNA that positively regulate the transcription of their target genes. It is estimated that there are hundreds of thousands of enhancers in the human genome, which is too large a task to check experimentally. By applying machine learning methods, we could obtain models of adequate accuracy to predict if a DNA sequence is an enhancer or not. This thesis observes three classes of machine learning methods: support vector machine SVM, multilayer perceptron neural network MLP, and ensemble learning. The results of our models are then compared to existing tools for identifying enhancers, iEnhancer-2L and iEnhancer-EL. We also provide a visualization of the predicted results for the different models on the same DNA sequence.
Keywords
Keywords (english)
Languageenglish
URN:NBNurn:nbn:hr:168:950014
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 15:38:56