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master's thesis
Predviđanje mikrobioma tla metodama strojnog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. urn:nbn:hr:168:891141
Šandrk, Marko
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Šandrk, M. (2022). Predviđanje mikrobioma tla 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:168:891141

Šandrk, Marko. "Predviđanje mikrobioma tla metodama strojnog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:891141

Šandrk, Marko. "Predviđanje mikrobioma tla metodama strojnog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:891141

Šandrk, M. (2022). 'Predviđanje mikrobioma tla metodama strojnog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 21 June 2024, https://urn.nsk.hr/urn:nbn:hr:168:891141

Šandrk M. Predviđanje mikrobioma tla metodama strojnog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 June 21] Available at: https://urn.nsk.hr/urn:nbn:hr:168:891141

M. Šandrk, "Predviđanje mikrobioma tla 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:168:891141

Metadata
TitlePredviđanje mikrobioma tla metodama strojnog učenja
Title (english)Machine learning methods for soil microbiome prediction
AuthorMarko Šandrk
MentorMirjana Domazet-Lošo (mentor)
Committee memberMirjana Domazet-Lošo (predsjednik povjerenstva)
Committee memberIvica Botički (član povjerenstva)
Committee memberTomislav Jagušt (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-02-23, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Razvojem novih metoda visokopropusnog sekvenciranja dolazi se do sve većih količina genomskih i metagenomskih podataka. Za analizu tih podataka sve se češće koriste i algoritmi strojnog i dubokog učenja. Mikrobiom, zajednica mikroorganizama koja obitava u nekoj okolini jest ključni stup života na Zemlji te bolje razumijevanje ovih zajednica može imati pozitivne učinke od ljudskog zdravlja do ekonomije. U sklopu rada razvijen je niz modeli dubokog učenja bazirani na nepotpunom te regulariziranom autoenkoderu za predviđanje mikrobioma tla na temelju različitih značajki okoliša kao što su temperatura, oborine, prisutna kultura i slično. Posebno se dobrim pokazala uporaba regulariziranih autoenkodera koji su postigli čak i nešto bolje rezultate od modela razvijenih u studiji (García-Jiménez et al., 2020).
Abstract (english)
With the development of new high throughput sequencing methods more and more genomic and metagenomic data is gathered. Machine and deep learning methods are used for analysis of such data more frequently. Microbiome, a community of microorganisms cohabitating in some dedicated environment, is a vital part of life on the planet Earth. Better understanding of these communities could have positive effects on human health as well as economy. In this thesis, several deep learning models are developed for purposes of predicting soil microbiome based on a few environmental features such as temperature, precipitation etc. All trained models were based on an undercomplete or a regularized autoencoder architecture. Using regularized autoencoders proved particularly convenient for this problem achieving slightly better scores on used metrics than those provided by (García-Jiménez et al., 2020).
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:891141
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 16:13:58