Meta-učenje z uporabo razultatov analize obogatenosti skupin genov

diplomsko delo visokošolskega strokovnega študijskega programa

Dario Šnajder (Author), Milan Zorman (Mentor), Peter Kokol (Co-mentor)

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;bioniformatika;odločitvena drevesa;

Data

Language:	Slovenian
Year of publishing:	2009
Source:	Maribor
Typology:	2.11 - Undergraduate Thesis
Organization:	UM FERI - Faculty of Electrical Engineering and Computer Science
Publisher:	[D. Šnajder]
UDC:	004.89(043.2)
COBISS:	13678358
Views:	2572
Downloads:	166
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	English
Secondary title:	Using gene set enrichment analysis results for meta-learning
Secondary 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.
Secondary keywords:	machine learning;meta leaning;bioinformatcs;decision trees;
URN:	URN:SI:UM:
Type (COBISS):	Undergraduate thesis
Thesis comment:	Univ. v Mariboru, Fak. za elektrotehniko, računalništvo in informatiko
Pages:	VIII, 53, [2] f.
Keywords (UDC):	science and knowledge;organization;computer science;information;documentation;librarianship;institutions;publications;znanost in znanje;organizacije;informacije;dokumentacija;bibliotekarstvo;institucije;publikacije;prolegomena;fundamentals of knowledge and culture;propaedeutics;prolegomena;splošne osnove znanosti in kulture;computer science and technology;computing;data processing;računalniška znanost in tehnologija;računalništvo;obdelava podatkov;artificial intelligence;umetna inteligenca;
ID:	987457