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FREQUENT SUBGRAPH MINING OF PERSONALIZED SIGNALING NETWORKS

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2017, Master of Sciences, Case Western Reserve University, Systems Biology and Bioinformatics.
In this study we have investigated the problem of identifying cancer subtypes integrating gene expression and protein-protein interaction networks. We propose the use of frequent subgraph mining coupled with non-negative matrix factorization and show that mining frequently occurring dysregulation patterns can uncover features that group patients into clinically relevant subtypes. Identified features are also able to provide functional mechanisms for differential prognosis among patients. This approach can improve current subtype classifications and identify functionally novel features.
Gurkan Bebek (Advisor)
Mehmet Koyuturk (Committee Member)
Jean-Eudes Dazard (Committee Member)
98 p.

Recommended Citations

Citations

  • Durmaz, A. (2017). FREQUENT SUBGRAPH MINING OF PERSONALIZED SIGNALING NETWORKS [Master's thesis, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case149704489838836

    APA Style (7th edition)

  • Durmaz, Arda. FREQUENT SUBGRAPH MINING OF PERSONALIZED SIGNALING NETWORKS. 2017. Case Western Reserve University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case149704489838836.

    MLA Style (8th edition)

  • Durmaz, Arda. "FREQUENT SUBGRAPH MINING OF PERSONALIZED SIGNALING NETWORKS." Master's thesis, Case Western Reserve University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case149704489838836

    Chicago Manual of Style (17th edition)