ePrints.FRI - University of Ljubljana, Faculty of Computer and Information Science

Andrej Čopar (2014) . MSc thesis.

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    Protein-RNA interactions have an essential role in many cellular processes. Experimental analysis of 3D molecular structure is slow and difficult process. Consequently, computational methods, which successfully predict interaction sites and molecular conformations are needed. In this thesis we have defined a number of attributes to describe local properties of protein-RNA interactions using data on 3D structure of protein-RNA molecules. We have implemented a method that uses machine learning and optimization algorithm for prediction of protein-RNA interaction sites. Machine learning predictions are used to generate initial positions for optimization. Optimization algorithm uses scoring functions based on the distribution of 3D structural attributes to identify most likely positions of the RNA molecule interacting with a given protein. The accuracy of the proposed prediction model is comparable to results obtained with best existing methods.

    Item Type: Thesis (MSc thesis)
    Keywords: bioinformatics, protein-RNA interactions, structural analysis, prediction model, combinatorial optimization, molecular docking
    Number of Pages: 83
    Language of Content: Slovenian
    Mentor / Comentors:
    Name and SurnameIDFunction
    doc. dr. Tomaž Curk299Mentor
    Link to COBISS: http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1536019139)
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Item ID: 2898
    Date Deposited: 04 Feb 2015 12:06
    Last Modified: 09 Feb 2015 10:00
    URI: http://eprints.fri.uni-lj.si/id/eprint/2898

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