Naslov
Predviđanje bolesti iz metagenomskih podataka
(Metagenomics-based disease prediction)

Autori
Široki, Tin

Vrsta, podvrsta i kategorija rada
Ocjenski radovi, diplomski rad, diplomski

Fakultet
Fakultet elektrotehnike i računarstva

Mjesto
Zagreb

Datum
03.07

Godina
2018

Stranica
50

Mentor
Domazet-Lošo, Mirjana

Ključne riječi
metagenomika ; mikrobiota ; predviđanje bolest ; strojno učenje
(Metagenomics ; Microbiota ; Disease-prediction ; Machine learning)

Sažetak
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).

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA