Functional classification of proteins using mass spectrometry data and exploration of their frequency of identification in proteomic analysis

• Κύριος συγγραφέας: Μπουγιούκος, Παναγιώτης
• Άλλοι συγγραφείς: Νικηφορίδης, Γεώργιος
• Μορφή: Thesis
• Γλώσσα: English
• Έκδοση: 2010
• Θέματα: Prostate cancer; Ovarian cancer; Processing and analysis of proteomic spectra; Pattern recognition; Καρκίνος του προστάτη; Καρκίνος των ωοθηκών; Αναγνώριση προτύπων; Επεξεργασία και ανάλυση πρωτεομικών φασμάτων; 616.650 75

Prostate cancer is a significant public health concern due to its high incidence and mortality, and that no consensus exists regarding the best form of treatment for any stage of the disease. Prostate cancer mortality can be reduced by the early prostate cancer detection. The earlier the detection the more effective the treatment would be. Prostate cancer screening or early detection has been accomplished applying the digital rectal examination (DRE) , the measurement of serum the prostate specific antigen (PSA), transrectal ultrasonography and combinations of these tests. MS based proteomics and particularly MS-SEDLI-TOF technology have assisted in discovering prostate cancer biomarkers. On the other hand, a major cause of mortality for women is the ovarian cancer. Malignant ovarian tumors are heterogeneous in their biological and clinical behaviour and a greater understanding of how they develop and progress is a prerequisite to successful early detection, screening programs, and treatment modalities. Accordingly, the aims of the present thesis are: (i) To develop a reliable pattern recognition system for the discrimination of healthy from patients with prostate cancer as well as controls from patients with ovarian cancer ,(ii) To develop efficient algorithms in order to handle the large number of features that are extracted from proteomic spectra, (iii) To develop a methodology to facilitate the investigation of the low intensity peaks which are the peaks in which biologists are mostly interested in, (iv) To propose potential biomarkers for discriminating healthy from prostate cancer cases and healthy from ovarian cancer cases . To cope with the above issues and in search of efficient methods for handling proteomic spectra a novel multi classifier pattern recognition methodology has been designed, developed and implemented, for the analysis of prostate and ovarian proteomic data. Furthermore, a novel method for splitting and grouping peaks according to their intensities has been developed to be consistent with biologist interest in investigating low intensity peaks.