Please use this identifier to cite or link to this item:
標題: Prediction of Ovarian Cancer Stages and Construction of Gene Network by Integrating Machine Learning and Bioinspired Algorithms
作者: 林守羿
Shou-Yi Lin
關鍵字: Ovarian cancer
Support vector machine
引用: [1]行政院衛生福利部 . (2014). 101年國人主要死因統計 .民國 101 年主要死因分析.doc [2]行政院衛生福利部 . (2014). 101 年死因性別統計分析 . [3]M. Schena, D. Shalon, R. W. Davis, and P. O. Brown, 'Quantitative monitoring of gene expression patterns with a complementary DNA microarray,' Science, vol. 270, pp. 467-70, Oct 20 1995. [4]顧祐瑞, 圖解生物學: 五南出版社, 2013. [5]C. C. Burges, 'A Tutorial on Support Vector Machines for Pattern Recognition,' Data Mining and Knowledge Discovery, vol. 2, pp. 121-167, 1998/06/01 1998. [6]C. Cortes and V. Vapnik, 'Support-Vector Networks,' Machine Learning, vol. 20, pp. 273-297, 1995/09/01 1995. [7]T.-J. Hsieh, H.-F. Hsiao, and W.-C. Yeh, 'Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm,' Applied Soft Computing, vol. 11, pp. 2510-2525, 3// 2011. [8]T.-J. Hsieh, H.-F. Hsiao, and W.-C. Yeh, 'Mining financial distress trend data using penalty guided support vector machines based on hybrid of particle swarm optimization and artificial bee colony algorithm,' Neurocomputing, vol. 82, pp. 196-206, 4/1/ 2012. [9]M. Dorigo, V. Maniezzo, and A. Colorni, 'Ant system: optimization by a colony of cooperating agents,' Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 26, pp. 29-41, 1996. [10]S. A. Hofmeyr and S. A. Forrest, 'Architecture for an Artificial Immune System,'Evol. Comput., vol. 8, pp. 443-473, 2000. [11]W. Shen, X. Guo, C. Wu, and D. Wu, 'Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm,' Knowledge-Based Systems, vol. 24, pp. 378-385, 4// 2011. [12]P. K. Sree, I. R. Babu, and N. S. Devi, 'Investigating an Artificial Immune System to strengthen protein structure prediction and protein coding region identification using the cellular automata classifier,' Int J Bioinform Res Appl, vol. 5, pp. 647-62, 2009. [13]Y. He and S. C. Hui, 'Exploring ant-based algorithms for gene expression data analysis,' Artif Intell Med, vol. 47, pp. 105-19, Oct 2009. [14]L. Y. Chuang, C. H. Yang, and C. H. Yang, 'Tabu search and binary particle swarm optimization for feature selection using microarray data,' J Comput Biol, vol. 16, pp. 1689-703, Dec 2009. [15]B. Duval and J. K. Hao, 'Advances in metaheuristics for gene selection and classification of microarray data,' Brief Bioinform, vol. 11, pp. 127-41, Jan 2010. [16]D. L. Tong and A. C. Schierz, 'Hybrid genetic algorithm-neural network: feature extraction for unpreprocessed microarray data,' Artif Intell Med, vol. 53, pp. 47- 56, Sep 2011. [17]C. Pang, G. Jiang, S. Wang, B. Hu, Q. Liu, Y. Deng, et al., 'Gene order computation using Alzheimer's DNA microarray gene expression data and the Ant Colony Optimisation algorithm,' Int J Data Min Bioinform, vol. 6, pp. 617-32, 2012. [18]E. A. Kheirelseid, N. Miller, K. H. Chang, C. Curran, E. Hennessey, M. Sheehan, et al., 'miRNA expressions in rectal cancer as predictors of response to neoadjuvant chemoradiation therapy,' Int J Colorectal Dis, vol. 28, pp. 247-60, Feb 2013. [19]A. Mehridehnavi and L. Ziaei, 'Minimal gene selection for classification and diagnosis prediction based on gene expression profile,' Adv Biomed Res, vol. 2, p. 26, 2013. [20]K. H. Chen, K. J. Wang, M. L. Tsai, K. M. Wang, A. M. Adrian, W. C. Cheng, et al., 'Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm,' BMC Bioinformatics, vol. 15, p. 49, 2014. [21]G. Chen, M. J. Cairelli, H. Kilicoglu, D. Shin, and T. C. Rindflesch, 'Augmenting microarray data with literature-based knowledge to enhance gene regulatory network inference,' PLoS Comput Biol, vol. 10, p. e1003666, Jun 2014. [22]Ingenuity IPA- Intergvate and understand complex'omics data. Available: Pathways Analysis [23]基礎教育訓練 . Available: [24]A. P. Heintz, F. Odicino, P. Maisonneuve, M. A. Quinn, J. L. Benedet, W. T. Creasman, et al., 'Carcinoma of the ovary. FIGO 26th Annual Report on the Results of Treatment in Gynecological Cancer,' Int J Gynaecol Obstet, vol. 95 Suppl 1, pp. S161-92, Nov 2006. [25]C. E. Shannon, 'A mathematical theory of communication,' ACM SIGMOBILE Mobile Computing and Communications Review, vol. 5, pp. 3-55, 2001. [26]J. R. Quinlan, 'Induction of decision trees,' Machine Learning, vol. 1, pp. 81-106, 1986/03/01 1986. [27] J. R. Quinlan, C4.5: programs for machine learning: Morgan Kaufmann Publishers Inc., 1993. [28] J. H. Holland, Adaptation in natural and artificial systems: MIT Press, 1992. [29] D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning: Addison-Wesley Longman Publishing Co., Inc., 1989. [30] M. Srinivas and L. M. Patnaik, 'Genetic algorithms: a survey,' Computer, vol. 27, pp. 17-26, 1994. [31] J. Kennedy and R. Eberhart, 'Particle swarm optimization,' in Neural Networks,1995. Proceedings., IEEE International Conference on, 1995, pp. 1942-1948 vol.4. [32] R. Eberhart and J. Kennedy, 'A new optimizer using particle swarm theory,' in Micro Machine and Human Science, 1995. MHS '95., Proceedings of the Sixth International Symposium on, 1995, pp. 39-43. [33] R. Eberhart and J. Kennedy, 'A new optimizer using particle swarm theory,' in Micro Machine and Human Science, 1995. MHS '95., Proceedings of the Sixth International Symposium on, 1995, pp. 39-43. [34] 劉家裕, '財務窘境預測基於改良式人工蜂群演算法與支援向量機之混和模型,' 碩士, 資訊管理系碩士班, 國立臺中科技大學, 台中市, 2013. [35] G. Callahan, S. R. Denison, L. A. Phillips, V. Shridhar, and D. I. Smith, 'Characterization of the common fragile site FRA9E and its potential role in ovarian cancer,' Oncogene, vol. 22, pp. 590-601, Jan 30 2003. [36] H. B. Boldt and C. A. Conover, 'Overexpression of pregnancy-associated plasma protein-A in ovarian cancer cells promotes tumor growth in vivo,' Endocrinology, vol. 152, pp. 1470-8, Apr 2011. [37] S. Y. Moon and Y. Zheng, 'Rho GTPase-activating proteins in cell regulation,' Trends Cell Biol, vol. 13, pp. 13-22, Jan 2003. [38] J. M. Berg, J. L. Tymoczko, and L. Stryer, 'Signal-transduction pathways: an introduction to information metabolism,' 2002. [39] T. Herold, V. Jurinovic, M. Mulaw, T. Seiler, A. Dufour, S. Schneider, et al., 'Expression analysis of genes located in the minimally deleted regions of 13q14 and 11q22-23 in chronic lymphocytic leukemia-unexpected expression pattern of the RHO GTPase activator ARHGAP20,' Genes Chromosomes Cancer, vol. 50, pp. 546-58, Jul 2011. [40] Z. Liang, L. H. Gao, L. J. Cao, D. Y. Feng, Y. Cao, Q. Z. Luo, et al., 'Detection of STAT2 in early stage of cervical premalignancy and in cervical cancer,' Asian Pac J Trop Med, vol. 5, pp. 738-42, Sep 2012. [41] A. M. Gamero, M. R. Young, R. Mentor-Marcel, G. Bobe, A. J. Scarzello, J. Wise, et al., 'STAT2 contributes to promotion of colorectal and skin carcinogenesis,' Cancer Prev Res (Phila), vol. 3, pp. 495-504, Apr 2010. [42] U. Wazir, A. Kasem, A. Sharma, W. Jiang, and K. Mokbel, 'Evidence for anti- apoptosis function of GNB1 in human breast cancer,' Cancer Research, vol. 72, 2012. [43] R. Schmitz, R. M. Young, M. Ceribelli, S. Jhavar, W. Xiao, M. Zhang, et al., 'Burkitt lymphoma pathogenesis and therapeutic targets from structural and functional genomics,' Nature, vol. 490, pp. 116-20, Oct 4 2012. [44] Q. Zhang, K. Sakamoto, C. Liu, A. A. Triplett, W. C. Lin, H. Rui, et al., 'Cyclin D3 compensates for the loss of cyclin D1 during ErbB2-induced mammary tumor initiation and progression,' Cancer Res, vol. 71, pp. 7513-24, Dec 15 2011. [45] J. M. Denu and J. E. Dixon, 'Protein tyrosine phosphatases: mechanisms of catalysis and regulation,' Curr Opin Chem Biol, vol. 2, pp. 633-41, Oct 1998. [46] S. Paul and P. J. Lombroso, 'Receptor and nonreceptor protein tyrosine phosphatases in the nervous system,' Cell Mol Life Sci, vol. 60, pp. 2465-82, Nov 2003. [47] K. Misawa, T. Kanazawa, Y. Misawa, A. Imai, S. Endo, K. Hakamada, et al., 'Hypermethylation of collagen alpha2 (I) gene (COL1A2) is an independent predictor of survival in head and neck cancer,' Cancer Biomark, vol. 10, pp. 135- 44, 2011. [48] L. Loss, A. Sadanandam, S. Durinck, S. Nautiyal, D. Flaucher, V. Carlton, et al., 'Prediction of epigenetically regulated genes in breast cancer cell lines,' BMC Bioinformatics, vol. 11, pp. 1-14, 2010/06/04 2010. [49] A. Christiansen and M. Detmar, 'Lymphangiogenesis and cancer,' Genes Cancer, vol. 2, pp. 1146-58, Dec 2011. [50] N. Karin, 'The multiple faces of CXCL12 (SDF-1alpha) in the regulation of immunity during health and disease,' J Leukoc Biol, vol. 88, pp. 463-73, Sep 2010. [51] T. Okegawa, K. Ushio, M. Imai, M. Morimoto, and T. Hara, 'Orphan nuclear receptor HNF4G promotes bladder cancer growth and invasion through the regulation of the hyaluronan synthase 2 gene,' Oncogenesis, vol. 2, p. e58, 2013. [52] J. Helleman, M. P. Jansen, K. Ruigrok-Ritstier, I. L. van Staveren, M. P. Look, M. E. Meijer-van Gelder, et al., 'Association of an extracellular matrix gene cluster with breast cancer prognosis and endocrine therapy response,' Clin Cancer Res, vol. 14, pp. 5555-64, Sep 1 2008. [53] Y. Imbert-Fernandez, B. F. Clem, J. O'Neal, D. A. Kerr, R. Spaulding, L. Lanceta, et al., 'Estradiol stimulates glucose metabolism via 6-phosphofructo-2-kinase (PFKFB3),' J Biol Chem, vol. 289, pp. 9440-8, Mar 28 2014. [54] B. F. Clem, J. O'Neal, G. Tapolsky, A. L. Clem, Y. Imbert-Fernandez, D. A. Kerr, 2nd, et al., 'Targeting 6-phosphofructo-2-kinase (PFKFB3) as a therapeutic strategy against cancer,' Mol Cancer Ther, vol. 12, pp. 1461-70, Aug 2013. [55] N. S. Anderson, L. Turner, S. Livingston, R. Chen, S. V. Nicosia, and P. A. Kruk, 'Bcl-2 expression is altered with ovarian tumor progression: an immunohistochemical evaluation,' J Ovarian Res, vol. 2, p. 16, 2009. [56] J. Witham, M. R. Valenti, A. K. De-Haven-Brandon, S. Vidot, S. A. Eccles, S. B. Kaye, et al., 'The Bcl-2/Bcl-XL family inhibitor ABT-737 sensitizes ovarian cancer cells to carboplatin,' Clin Cancer Res, vol. 13, pp. 7191-8, Dec 1 2007. [57] E. J. Nam, H. Yoon, S. W. Kim, H. Kim, Y. T. Kim, J. H. Kim, et al., 'MicroRNA expression profiles in serous ovarian carcinoma,' Clin Cancer Res, vol. 14, pp. 2690-5, May 1 2008. [58] F. McPhillips, P. Mullen, K. G. MacLeod, J. M. Sewell, B. P. Monia, D. A. Cameron, et al., 'Raf-1 is the predominant Raf isoform that mediates growth factor-stimulated growth in ovarian cancer cells,' Carcinogenesis, vol. 27, pp. 729-39, Apr 2006. [59] F. Li, D. N. Chen, C. W. He, Y. Zhou, V. M. Olkkonen, N. He, et al., 'Identification of urinary Gc-globulin as a novel biomarker for bladder cancer by two- dimensional fluorescent differential gel electrophoresis (2D-DIGE),' J Proteomics, vol. 77, pp. 225-36, Dec 21 2012. [60] W. Liu, B. Liu, Q. Cai, J. Li, X. Chen, and Z. Zhu, 'Proteomic identification of serum biomarkers for gastric cancer using multi-dimensional liquid chromatography and 2D differential gel electrophoresis,' Clin Chim Acta, vol. 413, pp. 1098-106, Jul 11 2012. [61] G. Karlebach and R. Shamir, 'Modelling and analysis of gene regulatory networks,' Nat Rev Mol Cell Biol, vol. 9, pp. 770-80, Oct 2008. [62] L. A. Begley, J. W. MacDonald, M. L. Day, and J. A. Macoska, 'CXCL12 activates a robust transcriptional response in human prostate epithelial cells,' J Biol Chem, vol. 282, pp. 26767-74, Sep 14 2007. [63] A. K. Ghosh, S. Bhattacharyya, J. Wei, S. Kim, Y. Barak, Y. Mori, et al., 'Peroxisome proliferator-activated receptor-gamma abrogates Smad-dependent collagen stimulation by targeting the p300 transcriptional coactivator,' Faseb j, vol. 23, pp. 2968-77, Sep 2009. [64] C. Wilson, E. Cajulis, J. Green, T. Olsen, Y. Chung, M. Damore, et al., 'HER-2 overexpression differentially alters transforming growth factor-β responses in luminal versus mesenchymal human breast cancer cells,' Breast Cancer Research, vol. 7, pp. 1-22, 2005/11/08 2005. [65] T. L. Yeung, C. S. Leung, K. K. Wong, G. Samimi, M. S. Thompson, J. Liu, et al., 'TGF-beta modulates ovarian cancer invasion by upregulating CAF-derived versican in the tumor microenvironment,' Cancer Res, vol. 73, pp. 5016-28, Aug 15 2013. [66] T.-V. Do, L. A. Kubba, H. Du, C. D. Sturgis, and T. K. Woodruff, 'Transforming growth factor-β1, transforming growth factor-β2, and transforming growth factor- β3 enhance ovarian cancer metastatic potential by inducing a Smad3-dependent epithelial-to-mesenchymal transition,' Molecular Cancer Research, vol. 6, pp. 695-705, 2008. [67] M. K. McConechy, J. Ding, J. Senz, W. Yang, N. Melnyk, A. A. Tone, et al., 'Ovarian and endometrial endometrioid carcinomas have distinct CTNNB1 and PTEN mutation profiles,' Mod Pathol, vol. 27, pp. 128-34, Jan 2014. [68] D. Maeda, J. Shibahara, T. Sakuma, M. Isobe, S. Teshima, M. Mori, et al., 'beta- catenin (CTNNB1) S33C mutation in ovarian microcystic stromal tumors,' Am J Surg Pathol, vol. 35, pp. 1429-40, Oct 2011. [69] Y. Jiao, W. Ou, F. Meng, H. Zhou, and A. Wang, 'Targeting HSP90 in ovarian cancers with multiple receptor tyrosine kinase coactivation,' Mol Cancer, vol. 10, p. 125, 2011. [70] M. Anttonen, M. Pihlajoki, N. Andersson, A. Georges, D. L'Hôte, S. Vattulainen, et al., 'FOXL2, GATA4, and SMAD3 co-operatively modulate gene expression, cell viability and apoptosis in ovarian granulosa cell tumor cells,' PloS one, vol. 9, p. e85545, 2014. [71] S. Glaysher, L. Bolton, P. Johnson, N. Atkey, M. Dyson, C. Torrance, et al., 'Targeting EGFR and PI3K pathways in ovarian cancer,' British journal of cancer, vol. 109, pp. 1786-1794, 2013. [72] Q. Sheng and J. Liu, 'The therapeutic potential of targeting the EGFR family in epithelial ovarian cancer,' British journal of cancer, vol. 104, pp. 1241-1245, 2011. [73] C. J. Nicol, M. Yoon, J. M. Ward, M. Yamashita, K. Fukamachi, J. M. Peters, et al., 'PPARgamma influences susceptibility to DMBA-induced mammary, ovarian and skin carcinogenesis,' Carcinogenesis, vol. 25, pp. 1747-55, Sep 2004. [74] S. Vignati, V. Albertini, A. Rinaldi, I. Kwee, C. Riva, R. Oldrini, et al., 'Cellular and molecular consequences of peroxisome proliferator-activated receptor- gamma activation in ovarian cancer cells,' Neoplasia, vol. 8, pp. 851-61, Oct 2006. [75] L. Cai, G. Zhang, X. Tong, Q. You, Y. An, Y. Wang, et al., 'Growth inhibition of human ovarian cancer cells by blocking STAT3 activation with small interfering RNA,' Eur J Obstet Gynecol Reprod Biol, vol. 148, pp. 73-80, Jan 2010. [76] L. Klampfer, 'Signal transducers and activators of transcription (STATs): Novel targets of chemopreventive and chemotherapeutic drugs,' Curr Cancer Drug Targets, vol. 6, pp. 107-21, Mar 2006. [77] J. V. Alvarez, H. Greulich, W. R. Sellers, M. Meyerson, and D. A. Frank, 'Signal transducer and activator of transcription 3 is required for the oncogenic effects of non-small-cell lung cancer-associated mutations of the epidermal growth factor receptor,' Cancer Res, vol. 66, pp. 3162-8, Mar 15 2006. [78] W. Yin, S. Cheepala, J. N. Roberts, K. Syson-Chan, J. DiGiovanni, and J. L. Clifford, 'Active Stat3 is required for survival of human squamous cell carcinoma cells in serum-free conditions,' Molecular cancer, vol. 5, p. 15, 2006. [79] T. Kusaba, T. Nakayama, K. Yamazumi, Y. Yakata, A. Yoshizaki, K. Inoue, et al., 'Activation of STAT3 is a marker of poor prognosis in human colorectal cancer,' Oncol Rep, vol. 15, pp. 1445-51, Jun 2006. [80] X. Zhu, Y. Li, C. Xie, X. Yin, Y. Liu, Y. Cao, et al., 'miR-145 sensitizes ovarian cancer cells to paclitaxel by targeting Sp1 and Cdk6,' International Journal of Cancer, pp. n/a-n/a, 2014. [81] Y. Bermudez, H. Yang, B. O. Saunders, J. Q. Cheng, S. V. Nicosia, and P. A. Kruk, 'VEGF- and LPA-induced telomerase in human ovarian cancer cells is Sp1- dependent,' Gynecol Oncol, vol. 106, pp. 526-37, Sep 2007. [82] D. G. Peters, D. M. Kudla, J. A. Deloia, T. J. Chu, L. Fairfull, R. P. Edwards, et al., 'Comparative gene expression analysis of ovarian carcinoma and normal ovarian epithelium by serial analysis of gene expression,' Cancer Epidemiol Biomarkers Prev, vol. 14, pp. 1717-23, Jul 2005. [83] J. A. Doherty, M. A. Rossing, K. L. Cushing-Haugen, C. Chen, D. J. Van Den Berg, A. H. Wu, et al., 'ESR1/SYNE1 polymorphism and invasive epithelial ovarian cancer risk: an Ovarian Cancer Association Consortium study,' Cancer Epidemiol Biomarkers Prev, vol. 19, pp. 245-50, Jan 2010. [84] J. W. McBroom, G. Acs, G. S. Rose, T. C. Krivak, A. Mohyeldin, and A. Verma, 'Erythropoietin receptor function and expression in epithelial ovarian carcinoma,' Gynecol Oncol, vol. 99, pp. 571-7, Dec 2005. [85] P. A. Muller and K. H. Vousden, 'p53 mutations in cancer,' Nature cell biology, vol. 15, pp. 2-8, 2013. [86] D. P. Lane, C. F. Cheok, and S. Lain, 'p53-based cancer therapy,' Cold Spring Harb Perspect Biol, vol. 2, p. a001222, Sep 2010. [87] B. Friedenson, 'The BRCA1/2 pathway prevents hematologic cancers in addition to breast and ovarian cancers,' BMC cancer, vol. 7, p. 152, 2007. [88] P. L. Welcsh and M.-C. King, 'BRCA1 and BRCA2 and the genetics of breast and ovarian cancer,' Human molecular genetics, vol. 10, pp. 705-713, 2001. [89] P. J. O'Donovan and D. M. Livingston, 'BRCA1 and BRCA2: breast/ovarian cancer susceptibility gene products and participants in DNA double-strand break repair,' Carcinogenesis, vol. 31, pp. 961-7, Jun 2010. [90] H. Kawamoto, H. Koizumi, and T. Uchikoshi, 'Expression of the G2-M checkpoint regulators cyclin B1 and cdc2 in nonmalignant and malignant human breast lesions: immunocytochemical and quantitative image analyses,' Am J Pathol, vol. 150, pp. 15-23, Jan 1997. [91] A. Wang, N. Yoshimi, N. Ino, T. Tanaka, and H. Mori, 'Overexpression of cyclin B1 in human colorectal cancers,' J Cancer Res Clin Oncol, vol. 123, pp. 124-7, 1997. [92] P. P. Claudio, A. Zamparelli, F. U. Garcia, L. Claudio, G. Ammirati, A. Farina, et al., 'Expression of cell-cycle-regulated proteins pRb2/p130, p107, p27(kip1), p53, mdm-2, and Ki-67 (MIB-1) in prostatic gland adenocarcinoma,' Clin Cancer Res, vol. 8, pp. 1808-15, Jun 2002. [93] V. Ouellet, T. H. Ling, K. Normandin, J. Madore, C. Lussier, V. Barres, et al., 'Immunohistochemical profiling of benign, low malignant potential and low grade serous epithelial ovarian tumors,' BMC Cancer, vol. 8, p. 346, 2008. [94] A. Jaggupilli and E. Elkord, 'Significance of CD44 and CD24 as cancer stem cell markers: an enduring ambiguity,' Journal of Immunology Research, vol. 2012, 2012. [95] D. R. Schwartz, R. Wu, S. L. Kardia, A. M. Levin, C. C. Huang, K. A. Shedden, et al., 'Novel candidate targets of beta-catenin/T-cell factor signaling identified by gene expression profiling of ovarian endometrioid adenocarcinomas,' Cancer Res, vol. 63, pp. 2913-22, Jun 1 2003.
摘要: Ovarian cancer is the common gynecological diseases. According to the statistics of Ministry of Health and Welfare; ovarian cancer is one of ten leading causes of women death. The main reason is because of no appreciable at the early stage of ovarian cancer and there are usually in the terminal stage when patient has been diagnosed with ovarian cancer. Furthermore, the biomarker CA125 is lack of specificity and sensitivity when selected ovarian cancer; it's also the reason that result high mortality rate of ovarian cancer. Hence, finding the biomarker which could detected ovarian cancer precisely is a crucial topic. The method of microarray analysis and data mining has been wildly used in the cancer research recently and achieved great result. Therefore, this research propose a method which use data mining algorithms build a model that can detect the phase of cancer research. This model has three steps, in the first step, this research used ovarian microarray as sample; and preliminary screening the target gene that could detected the ovarian cancer through the Information Gain which were ID3 and C4.5 algorithms. The second step is test the classification rate of target gene through four methods, GA-SVN、 PSO-SVM 、 ABC-SVM and DFABC-SVM which were meta-heuristic algorithms combine SVM, and then select the target gene which has top one classification rate. The final step is use the online biological database and analysis software, IPA, to build the genetic network of ovarian cancer gene; which help us understand the connection of each target gene in the different phase. In this study, we select PAPPA、STAT2、BCL2 gene, and we expect the result of this research can be the basis of clinical trial of ovarian cancer and help doctor diagnosis and find ovarian cancer in the early stage to increase the survival rate of patients.
卵巢癌為中西方常見的婦癌疾病。根據衛生署福利部統計,卵巢癌為 101 年台灣婦女十大癌症死因之一。主要原因在於卵巢癌在發病初期並沒有明顯特徵,病人檢測出罹癌時,大多已是癌症末期。此外腫瘤血清標誌 CA125 篩選卵巢癌之特異性與敏感性之不足,導致卵巢癌致死率居高不下。因此找出能夠精準檢測卵巢癌之標靶物是個迫切的議題。近年來,微陣列資料分析與資料探勘方法被廣泛應用在癌症研究領域中,並有不錯的成效。故本研究旨在利用資料探勘演算法,建立一組能夠檢測卵巢癌期別之模型。本研究以卵巢癌微陣列資料為樣本,先利用資訊獲利、ID3 演算法與 C4.5 演算法,初步篩選出足以檢測卵巢癌之標靶基因。再透過由仿生演算法所改良的四種 SVM 演算法:GA-SVN、PSO-SVM、ABC-SVM、DFABC-SVM,來對標靶基因進行分類準確度之檢測,並挑選出分類準確度高之標靶基因。最後使用線上生物資料庫與分析軟體 IPA,來建構卵巢癌標靶基因之基因網路,以了解在卵巢癌於不同病理期別中每個標靶基因之相互關係。本研究篩選出PAPPA、STAT2、BCL2 等標靶基因,並期望經由生物實驗驗證後,能作為未來卵巢癌篩檢以及臨床相關研究之依據,並輔助醫生進行卵巢癌診斷,以進行卵巢癌之早期治療,來提升卵巢癌病人之存活率。
其他識別: U0005-0206201511193200
文章公開時間: 2018-07-15
Appears in Collections:資訊管理學系



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.