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master's thesis
Predviđanje bolesti iz metagenomskih podataka
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. urn:nbn:hr:168:323544
Široki, Tin
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Široki, T. (2018). Predviđanje bolesti iz metagenomskih podataka (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:323544

Široki, Tin. "Predviđanje bolesti iz metagenomskih podataka." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. https://urn.nsk.hr/urn:nbn:hr:168:323544

Široki, Tin. "Predviđanje bolesti iz metagenomskih podataka." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. https://urn.nsk.hr/urn:nbn:hr:168:323544

Široki, T. (2018). 'Predviđanje bolesti iz metagenomskih podataka', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 22 July 2024, https://urn.nsk.hr/urn:nbn:hr:168:323544

Široki T. Predviđanje bolesti iz metagenomskih podataka [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2018 [cited 2024 July 22] Available at: https://urn.nsk.hr/urn:nbn:hr:168:323544

T. Široki, "Predviđanje bolesti iz metagenomskih podataka", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2018. Available at: https://urn.nsk.hr/urn:nbn:hr:168:323544

Metadata
TitlePredviđanje bolesti iz metagenomskih podataka
Title (english)Metagenomics-based disease prediction
AuthorTin Široki
MentorMirjana Domazet-Lošo (mentor)
Committee memberMirjana Domazet-Lošo (predsjednik povjerenstva)
Committee memberKrešimir Fertalj (član povjerenstva)
Committee memberBoris Milašinović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2018-07-03, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Razvoj novih tehnologija sekvenciranja omogućio je metagenomske analize, tj. analize uzoraka izravno prikupljenih iz okoliša bez potrebe za uzgojem pojedinih vrsta u laboratorijskim uvjetima. U okviru ovog diplomskog rada istražen je problem predviđanja bolesti tehnikama strojnog učenja iz podataka o mikrobiomu prikupljenih metagenomskim analizama. Rad sadrži pregled postojeće literature, i rezultate evaluacije modela stroja potpornih vektora (SVM), AdaBoost (jačanje stabla odluka), slučajne šume, i umjetnih neuronskih mreža na tri skupa podataka koji sadrže kontrolne uzorke i uzorke koji su pogođeni cirozom jetre, karcinomom debelog crijeva i dijabetesom tipa 2. Najbolji postignuti F1 rezultati su: 0.89 na skupu podataka za cirozu jetre (AdaBoost), 0.81 na skupu podataka za karcinom debelog crijeva (AdaBoost), i 0.76 na skupu podataka za dijabetes tipa 2 (SVM).
Abstract (english)
The development of high-throughput sequencing technologies has enabled large-scale metagenomic analyses, i.e. direct analyses of all genomes in samples with no need for the cultivation of specific species. In this thesis, the problem of machine learning based disease prediction from metagenomic microbiome data is addressed. The thesis contains a survey of recent literature, and the evaluation of support vector machine, AdaBoost (boosting decision trees), random forest, and artificial neural network models on three different datasets containing control samples and samples affected with liver cirrhosis, colorectal cancer, and type two diabetes. The best F1 scores are: 0.89 on the liver cirrhosis dataset (AdaBoost), 0.81 on the colorectal cancer dataset (AdaBoost), and 0.76 on the type two diabetes dataset (SVM).
Keywords
Keywords (english)
Languageenglish
URN:NBNurn:nbn:hr:168:323544
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 on2019-03-14 01:17:35