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Topološki pristopi k analizi bioloških podatkov
Đurđević, Marija (Author), Mramor Kosta, Nežka (Mentor) More about this mentor... This link opens in a new window, Zupan, Blaž (Co-mentor)

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Abstract
Podatki o genski izraženosti rakavega tkiva imajo napovedno vrednost pri napovedovanju bolnikovega kliničnega izida. Na področju rakavih bolezni je pomembno ugotavljanje podkategorije v posamezni kategoriji raka. V magistrski nalogi smo se za reševanje tega problema odločili za implementacijo algoritmov, ki temeljijo na računski topologiji. Cilj naloge je, da z računanjem vztrajne homologije na podatkih o genski izraženosti rakavega tkiva ugotovimo nove podskupine ter poskusimo napovedovati preživetje bolnikov v skupinah. Podatki, ki smo jih analizirali, izhajajo iz podatkovne zbirke mednarodnega konzorcija za genske raziskave raka ICGC. Na omenjenih podatkih smo gradili simplicialne komplekse pri različnih resolucijah z uporabo algoritma Vietoris-Rips. Nato smo računali vztrajno homologijo in izrisovali vztrajne diagrame. Z namenom, da čim bolj natančno ločimo podkategorije raka, smo razvili metodo za računanje intervala zaupanja na vztrajnih diagramih. Na ta način smo uspešno odkrili nekaj novih podskupin ter napovedali klinični izid bolnikov. Uspeh metod smo ovrednotili na podatkih z več različnih tipov raka ter rezultate uspešno primerjali z drugimi metodami nenadzorovanega učenja.

Language:Slovenian
Keywords:topologija, topološka analiza podatkov, simplicialni kompleks, Vietoris-Rips, rak, klasifikacijske metode, krivulja preživetja
Work type:Master's thesis/paper (mb22)
Organization:FRI - Faculty of computer and information science
Year:2016
Views:671
Downloads:291
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Secondary language

Language:English
Title:Topological Approach to Analyses of Omics Data
Abstract:
Genes expression is often a good indicator for prediction of patient's clinical results. In diseases such as cancer is inevitable to identify subcategories of phenotype. The goal of the Thesis is to use persistent homology on cancer tissue gene expression to identify new subgroups and try to predict the survival of patients in corresponding groups. We analyse the date from the International Consortium for Cancer Research. Simplicial complexes were built different resolutions using Vietoris-Rips algorithm. We counted the persistent homology and draw persistent diagrams. We developed a method for calculating confidence interval on persistent diagrams to precisely divide cancer subcategories. This method gave us promising results by discovering new subcategories and was accurate in prediction of patient clinical results. Results were obtained on data of different cancer types. Results were compared with different unsupervised learning methods.

Keywords:topology, topological data analysis, simplicial complex, Vietoris-Rips, cancer, classification methods, survival curves

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