Nástroj pro predikci atributů životního stylu na základě metagenomických dat z tlustého střeva

This thesis deals with analysis of human microbiome using metagenomic data from large intestine. The main focus is placed on bacteria composition in a sample on different taxonomic levels regarding the lifestyle traits of an individual. For this purpose, a tool for classification of several attributes was created. It considers attributes like diet type and eating habits (vegetarian, vegan, omnivore), gluten and lactose intolerance, body mass index, age or sex. From range of machine learning perspectives considering K Nearest Neighbours (kNN), Random Forest (RF) and Support Vector Machines (SVM) were used. Datasets for training and final evaluation of the classifier were taken from American Gut project. The thesis also focuses on particular problems with metagenomic datasets like its multidimensionality, sparsity, compositional character and class imbalance.

metagenomics, taxonomy, OTU, prediction, classification, machine learning, K Nearest Neighbours, Support Vector Machines, Random Forest, T-test, Principal Component Analysis, Linear Discriminant Analysis