• Login
    View Item 
    •   Home
    • Theses and Dissertations
    • Dissertations
    • View Item
    •   Home
    • Theses and Dissertations
    • Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguidePlumX LibguideSubmit an Item

    Statistics

    Display statistics

    Machine Learning Identification of Protein Properties Useful for Specific Applications

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    PhD Dissertation_Final copy.pdf
    Size:
    5.777Mb
    Format:
    PDF
    Download
    Type
    Dissertation
    Authors
    Khamis, Abdullah M. cc
    Advisors
    Bajic, Vladimir B. cc
    Committee members
    Gojobori, Takashi cc
    Tegner, Jesper cc
    Gao, Xin cc
    Program
    Computer Science
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2016-03-31
    Embargo End Date
    2017-04-20
    Permanent link to this record
    http://hdl.handle.net/10754/606030
    
    Metadata
    Show full item record
    Access Restrictions
    At the time of archiving, the student author of this dissertation opted to temporarily restrict access to it. The full text of this dissertation became available to the public after the expiration of the embargo on 2017-04-20.
    Abstract
    Proteins play critical roles in cellular processes of living organisms. It is therefore important to identify and characterize their key properties associated with their functions. Correlating protein’s structural, sequence and physicochemical properties of its amino acids (aa) with protein functions could identify some of the critical factors governing the specific functionality. We point out that not all functions of even well studied proteins are known. This, complemented by the huge increase in the number of newly discovered and predicted proteins, makes challenging the experimental characterization of the whole spectrum of possible protein functions for all proteins of interest. Consequently, the use of computational methods has become more attractive. Here we address two questions. The first one is how to use protein aa sequence and physicochemical properties to characterize a family of proteins. The second one focuses on how to use transcription factor (TF) protein’s domains to enhance accuracy of predicting TF DNA binding sites (TFBSs). To address the first question, we developed a novel method using computational representation of proteins based on characteristics of different protein regions (N-terminal, M-region and C-terminal) and combined these with the properties of protein aa sequences. We show that this description provides important biological insight about characterization of the protein functional groups. Using feature selection techniques, we identified key properties of proteins that allow for very accurate characterization of different protein families. We demonstrated efficiency of our method in application to a number of antimicrobial peptide families. To address the second question we developed another novel method that uses a combination of aa properties of DNA binding domains of TFs and their TFBS properties to develop machine learning models for predicting TFBSs. Feature selection is used to identify the most relevant characteristics of the aa for such modeling. In addition to reducing the number of required models to only 14 for several hundred TFs, the final prediction accuracy of our models appears dramatically better than with other methods. Overall, we show how to efficiently utilize properties of proteins in deriving more accurate solutions for two important problems of computational biology and bioinformatics. 
    Citation
    Khamis, A. M. (2016). Machine Learning Identification of Protein Properties Useful for Specific Applications. KAUST Research Repository. https://doi.org/10.25781/KAUST-14K4P
    DOI
    10.25781/KAUST-14K4P
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-14K4P
    Scopus Count
    Collections
    Dissertations; Computer Science Program; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

    entitlement

     
    DSpace software copyright © 2002-2021  DuraSpace
    Quick Guide | Contact Us | KAUST University Library
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.