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Title:Rudarjenje asociativnih pravil z evolucijskimi algoritmi : magistrsko delo
Authors:ID Božič, Katja (Author)
ID Fister, Iztok (Mentor) More about this mentor... New window
ID Gorenjak, Mario (Comentor)
Files:.pdf MAG_Bozic_Katja_2025.pdf (1,74 MB)
MD5: 6066196187FC0D8DF349163A0FDB0C17
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V magistrskem delu smo raziskali uporabo evolucijskih algoritmov za rudarjenje asociativnih pravil pri analizi genetskih podatkov na področju bioinformatike. Evolucijske algoritme smo uporabili za odkrivanje znanja iz podatkov, kjer nam asociacijska pravila prikažejo skrite povezave med atributi v podatkovni množici. S pomočjo evolucijskih algoritmov za rudarjenje asociativnih pravil smo na genetskih podatkih zdravih tkiv in tkiv z miomi maternice naredili analizo, s katero smo odkrili neznane povezave med geni. Ugotovili smo, da lahko evolucijske algoritme za rudarjenje asociativnih pravil uporabimo za odkrivanje genetskih vzorcev. Pridobljene podatke bi lahko uporabili za razumevanje molekularnih mehanizmov bolezni, kar bi prispevalo k napredku v diagnostiki in zdravljenju bolezni.
Keywords:bioinformatika, evolucijski algoritmi, geni, miomi maternice, rudarjenje asociativnih pravil
Publication status:Published
Publication version:Version of Record
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[K. Božič]
Year of publishing:2025
Number of pages:1 spletni vir (1 datoteka PDF (XIII, 79 str.))
PID:20.500.12556/DKUM-92234 New window
UDC:[004.021:575.82]:004.83(043.2)
COBISS.SI-ID:236169987 New window
Publication date in DKUM:08.05.2025
Views:153
Downloads:56
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:24.03.2025

Secondary language

Language:English
Title:Evolutionary algorithms for association rule mining
Abstract:In this master thesis, we investigated the use of evolutionary algorithms for association rule mining to analyze genetic data in bioinformatics. We used evolutionary algorithms to discover knowledge from data, where association rules show us hidden connections between attributes in a dataset. With the help of evolutionary algorithms for association rule mining, we analyzed the genetic data of healthy tissues and tissues with uterine fibroids to discover unknown connections between genes. We found that evolutionary algorithms for association rule mining can be used to discover genetic patterns. The obtained data could be used to understand the molecular mechanisms of diseases, which would contribute to advances in the diagnosis and treatment of diseases.
Keywords:bioinformatics, evolutionary algorithms, genes, uterine fibroids, association rule mining


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