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Title:META-UČENJE Z UPORABO REZULTATOV ANALIZE OBOGATENOSTI SKUPIN GENOV
Authors:Šnajder, Dario (Author)
Zorman, Milan (Mentor) More about this mentor... New window
Files:.pdf VS_Snajder_Dario_1985.pdf (11,63 MB)
 
Language:Slovenian
Work type:Undergraduate thesis (m5)
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V diplomskem delu smo proučili in pokazali možnost uporabe postopkov meta učenja za proučevanje delovanja algoritma GSEA. Napisali smo aplikacijo, v kateri smo uvedli večnitno izvajanje in implementirali upravitelja opravil. Ta nam omogoča spremljanje poteka izvajanja, s tem pa obveščanje uporabnika o stanju v aplikaciji. V začetku diplomske naloge smo opisali formate datotek, katere uporabljamo, nato smo opisali strojno učenje in njegovo podpoglavje meta-učenje. Nadaljevali smo s postopki izvajanja GSEA analize in gradnjo odločitvenih dreves. Diplomsko nalogo smo zaključili s sklepom, v katerem smo navedli možnosti za nadaljnje raziskovanje.
Keywords:strojno učenje, meta-učenje, bioinformatika, odločitvena drevesa
Year of publishing:2009
Publisher:[D. Šnajder]
Source:Maribor
UDC:004.89(043.2)
COBISS_ID:13678358 Link is opened in a new window
Views:2033
Downloads:111
Metadata:XML RDF-CHPDL DC-XML DC-RDF
Categories:KTFMB - FERI
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Secondary language

Language:English
Title:USING GENE SET ENRICHMENT ANALYSIS RESULTS FOR META-LEARNING
Abstract:In diploma work we demonstrate a way to use meta learning concepts to study results of GSEA algorithm that is widely used in bioinformatics. We developed an application where we introduced multitasking and implemented task manager. This enables monitoring progress, thereby informing the user of the state of application. At the beginning of the diploma work we examined the use of file formats. In the following sections we describe machine learning and meta learning concepts. We continue with the GSEA analysis and building of decision trees. Diploma work concludes with the final section in which we have indicated the potential for further exploration.
Keywords:machine learning, meta learning, bioinformatics, decision trees


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