Thesis Details
Strojové učení v úloze predikce vlivu nukleotidového polymorfismu
This thesis brings a new approach to the prediction of the effect of nucleotide polymorphism on human genome. The main goal is to create a new meta-classifier, which combines predictions of several already implemented software classifiers. The novelty of developed tool lies in using machine learning methods to find consensus over those tools, that would enhance accuracy and versatility of prediction. Final experiments show, that compared to the best integrated tool, the meta-classifier increases the area under ROC curve by 3,4 in average and normalized accuracy is improved by up to 7\,\%. The new classifying service is available at http://ll06.sci.muni.cz:6232/snpeffect/.
Deoxyribonucleic acid, protein, mutation, polymorphism, prediction, training dataset, machine learning, ensemble learning
Bartík Vladimír, Ing., Ph.D. (DIFS FIT BUT), člen
Holík Lukáš, doc. Mgr., Ph.D. (DITS FIT BUT), člen
Martínek Tomáš, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Šaloun Petr, doc. RNDr., Ph.D. (VŠB-TUO), člen
Zbořil František, doc. Ing., Ph.D. (DITS FIT BUT), člen
@mastersthesis{FITMT16983, author = "Ond\v{r}ej \v{S}alanda", type = "Master's thesis", title = "Strojov\'{e} u\v{c}en\'{i} v \'{u}loze predikce vlivu nukleotidov\'{e}ho polymorfismu", school = "Brno University of Technology, Faculty of Information Technology", year = 2015, location = "Brno, CZ", language = "czech", url = "https://www.fit.vut.cz/study/thesis/16983/" }