Title
Računarske metode particionisanja i grupisanja u biološkim mrežama
Creator
Grbić, Milana, 1989-, 57188105
Copyright date
2020
Object Links
Select license
Autorstvo-Nekomercijalno-Bez prerade 3.0 Srbija (CC BY-NC-ND 3.0)
License description
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Language
Serbian
Cobiss-ID
Theses Type
Doktorska disertacija
description
Datum odbrane: 07.07.2020.
Other responsibilities
mentor
Pavlović-Lažetić, Gordana, 1955-, 12443239
član komisije
Filipović, Vladimir, 1968-, 12759399
član komisije
Kartelj, Aleksandar, 1986-, 57190665
član komisije
Matić, Dragan, 1977-, 52530185
član komisije
Gemović, Branislava, 1982-, 57566985
Academic Expertise
Prirodno-matematičke nauke
Academic Title
-
University
Univerzitet u Beogradu
Alternative title
Computational methods for partitioning and grouping in biological networks
Publisher
[ M. Grbić]
Format
121 str.
description
Računarstvo-
Bioinformatika / Computer Science-
Bioinformatics
Abstract (sr)
U ovoj disertaciji se istražuju aktuelni problemi bioinformatike i računarske biologije i metode za njihovo rješavanje...
Abstract (en)
In this dissertation some actual problems of bioinformatics and computational biology are explored,
together with the methods for solving them. The following problems are considered: partitioning of
sparse biological networks into k-plex subnetworks, prediction of the role of metabolites in metabolic
reactions, partitioning of biological networks into highly connected components and the problem of
identification of significant groups of proteins by adding new edges to the weighted protein interactions
network. The aforementioned problems have theoretical importance in areas of machine learning
and optimization, and practical application in biological research. In addition to solving the aforementioned
problems from the computational aspect, the dissertation explores further application of
the obtained results in the fields of biology and biochemistry, as well as the integration of results
within existing bioinformatics tools.
The problem of predicting the role of metabolites in metabolic reactions is solved by a predictive
machine learning method based on the conditional random fields, while for the remaining three
problems the algorithams based on variable neighbourhood search are developed. For solving the
problem of identification of significant groups of proteins by adding new edges to the weighted protein
interactions network, the variable neighbourhood search is only the first phase of the proposed
solution, while in the second and the third phase of the proposed method, the integration with
additional biological information and bioinformatics tools are performed.
The proposed computational methods of partitioning and grouping in biological networks confirm
existing findings in a new manner and lead to new discoveries about biological elements and the
connections between them. By solving these problems and by interpreting the obtained results
in this dissertation, a scientific contribution was made to the scientific field of computer science,
particularly to the scientific disciplines of bioinformatics and computational biology.
Authors Key words
kombinatorna optimizacija, metoda promjenljivih okolina, uslovna slučajna polja, biološke mreže,
protein-protein interakcije, k-plex, visoko povezane komponente
Authors Key words
combinatorial optimization, variable neighborhood search, conditional random fields, biological networks,
protein-protein interaction k-plex, highly connected components
Classification
519.179.2:004.7:577(043.3)
Type
Tekst
Abstract (sr)
U ovoj disertaciji se istražuju aktuelni problemi bioinformatike i računarske biologije i metode za njihovo rješavanje...
“Data exchange” service offers individual users metadata transfer in several different formats. Citation formats are offered for transfers in texts as for the transfer into internet pages. Citation formats include permanent links that guarantee access to cited sources. For use are commonly structured metadata schemes : Dublin Core xml and ETUB-MS xml, local adaptation of international ETD-MS scheme intended for use in academic documents.