Your browser does not allow JavaScript!
JavaScript is necessary for the proper functioning of this website. Please enable JavaScript or use a modern browser.
|
|
SLO
|
ENG
|
Cookies and privacy
DKUM
EPF - Faculty of Business and Economics
FE - Faculty of Energy Technology
FERI - Faculty of Electrical Engineering and Computer Science
FF - Faculty of Arts
FGPA - Faculty of Civil Engineering, Transportation Engineering and Architecture
FKBV - Faculty of Agriculture and Life Sciences
FKKT - Faculty of Chemistry and Chemical Engineering
FL - Faculty of Logistic
FNM - Faculty of Natural Sciences and Mathematics
FOV - Faculty of Organizational Sciences in Kranj
FS - Faculty of Mechanical Engineering
FT - Faculty of Tourism
FVV - Faculty of Criminal Justice and Security
FZV - Faculty of Health Sciences
MF - Faculty of Medicine
PEF - Faculty of Education
PF - Faculty of Law
UKM - University of Maribor Library
UM - University of Maribor
UZUM - University of Maribor Press
COBISS
Faculty of Business and Economic, Maribor
Faculty of Agriculture and Life Sciences, Maribor
Faculty of Logistics, Celje, Krško
Faculty of Organizational Sciences, Kranj
Faculty of Criminal Justice and Security, Ljubljana
Faculty of Health Sciences
Library of Technical Faculties, Maribor
Faculty of Medicine, Maribor
Miklošič Library FPNM, Maribor
Faculty of Law, Maribor
University of Maribor Library
Bigger font
|
Smaller font
Introduction
Search
Browsing
Upload document
For students
For employees
Statistics
Login
First page
>
Show document
Show document
Title:
UGOTAVLJANJE GENSKIH PREDIKTORJEV S POMOČJO INTELIGENTNIH SISTEMOV
Authors:
ID
Rednjak, Nejc
(Author)
ID
Kokol, Peter
(Mentor)
More about this mentor...
Files:
MAG_Rednjak_Nejc_2015.pdf
(1,75 MB)
MD5: 41F31EEBFCA4DFBE428710BFAC7D2AEB
Language:
Slovenian
Work type:
Master's thesis/paper
Typology:
2.09 - Master's Thesis
Organization:
FZV - Faculty of Health Sciences
Abstract:
Pri strojnem učenju (angl. Machine Learning) gre za pridobivanje znanja na podlagi izkušenj. Ne gre za učenje na pamet, ampak za iskanje pravil v učnih podatkih. Najbolj znane metode strojnega učenja so odločitvena drevesa (DT), metoda podpornih vektorjev (SVM) in nevronske mreže (NN). Metode strojnega učenja so nam v pomoč pri ugotavljanju genskih prediktorjev. Algoritmi strojnega učenja imajo prav tako pomembno vlogo pri diagnosticiranju rakavih obolenj. V magistrskem delu smo opisali najbolj znane metode strojnega učenja in jih preizkusili na podatkovni bazi AP_Colon_Kidney. Uporabili smo podatkovno bazo iz spletne zbirke GEMLeR, ki vsebuje podatke o genski ekspresiji za več kot 2000 vzorcev tumorjev. Raziskali smo tudi, kateri geni so najbolj izraženi v primeru rakavega obolenja debelega črevesa in ledvic.
Keywords:
strojno učenje
,
odločitvena drevesa
,
nevronske mreže
,
metoda podpornih vektorjev
,
gen
,
podatkovna baza.
Place of publishing:
Maribor
Publisher:
[N. Rednjak]
Year of publishing:
2015
PID:
20.500.12556/DKUM-47809
UDC:
575(043.2)
COBISS.SI-ID:
2144932
NUK URN:
URN:SI:UM:DK:KMGLYBXF
Publication date in DKUM:
24.09.2015
Views:
3143
Downloads:
193
Metadata:
Categories:
FZV
Cite this work
Plain text
BibTeX
EndNote XML
EndNote/Refer
RIS
ABNT
ACM Ref
AMA
APA
Chicago 17th Author-Date
Harvard
IEEE
ISO 690
MLA
Vancouver
:
REDNJAK, Nejc, 2015,
UGOTAVLJANJE GENSKIH PREDIKTORJEV S POMOČJO INTELIGENTNIH SISTEMOV
[online]. Master’s thesis. Maribor : N. Rednjak. [Accessed 25 March 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=47809
Copy citation
Average score:
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
(0 votes)
Your score:
Voting is allowed only for
logged in
users.
Share:
Searching for similar works...
Hover the mouse pointer over a document title to show the abstract or click on the title to get all document metadata.
Secondary language
Language:
English
Title:
DETERMINING GENETIC PREDICTORS BY USING INTELLIGENT SYSTEMS
Abstract:
With machine learning we acquire knowledge based on experience. It is not about learning by memorization but to search the rules in the learning data. The most known representatives of machine learning are decision trees (DT), support vector machines (SVM) and neural networks (NN). Machine learning methods help us to identify genetic predictors. The machine learning algorithms also play an important role in cancer diagnosis. In our master thesis we describe the most known machine learning methods and test them on an AP_Colon_Kidney database. For these master thesis we have used a database from an GEMLeR online collection which contains data on gene expression with more than 2,000 samples of tumors. We have also investigated which genes are the most xpressed in the case of colon and kidney cancer.
Keywords:
machine learning
,
decision trees
,
neural networks
,
support vector machine
,
gene
,
database.
Comments
Leave comment
You must
log in
to leave a comment.
Comments (0)
0 - 0 / 0
There are no comments!
Back
