請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/7386完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 蕭朱杏 | zh_TW |
| dc.contributor.author | 林書如 | zh_TW |
| dc.contributor.author | Shu-Ju Lin | en |
| dc.date.accessioned | 2021-05-19T17:42:38Z | - |
| dc.date.available | 2024-08-17 | - |
| dc.date.copyright | 2019-03-05 | - |
| dc.date.issued | 2019 | - |
| dc.date.submitted | 2002-01-01 | - |
| dc.identifier.citation | Abba, M. C., Gong, T., Lu, Y., Lee, J., Zhong, Y., Lacunza, E., Butti, M., Takata, Y., Gaddis, S., Shen, J., Estecio, M. R., Sahin, A. A., Aldaz, C. M. (2015). A Molecular Portrait of High-Grade Ductal Carcinoma In Situ. Cancer research, 75(18), 3980-3990. doi:10.1158/0008-5472.CAN-15-0506
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, Allan P., Dolinski, K., Dwight, S. S., Eppig, J. T., Harris, M. A., Hill, D. P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J. C., Richardson, J. E., Ringwald, M., Rubin, G. M., Sherlock, G. (2000). Gene ontology: tool for the unification of biology. Nature Genetics, 25, 25-29. Bachmanov, A. A., Bosak, N. P., Lin, C., Matsumoto, I., Ohmoto, M., Reed, D. R., & Nelson, T. M. (2014). Genetics of Taste Receptors. Current pharmaceutical design, 20(16), 2669-2683. Barry, W. T., Nobel, A. B., & Wright, F. A. (2005). Significance analysis of functional categories in gene expression studies: a structured permutation approach. Bioinformatics, 21(9), 1943-1949. doi:10.1093/bioinformatics/bti260 Bijker, N., Meijnen, P., Peterse, J. L., Bogaerts, J., Van Hoorebeeck, I., Julien, J.-p., Gennaro, M., Rouanet, P., Avril, A., Fentiman, I. S. (2006). Breast-conserving treatment with or without radiotherapy in ductal carcinoma-in-situ: ten-year results of European Organisation for Research and Treatment of Cancer randomized phase III trial 10853—a study by the EORTC Breast Cancer Cooperative Group and EORTC Radiotherapy Group. Journal of Clinical Oncology, 24(21), 3381-3387. Celedón, J. C., Lange, C., Raby, B. A., Litonjua, A. A., Palmer, L. J., DeMeo, D. L., Reilly, J. J., Kwiatkowski, D. J., Chapman, H. A., Laird, N., Sylvia, J. S., Hernandez, M., Speizer, F. E., Weiss, S. T., Silverman, E. K. (2004). The transforming growth factor-β1 (TGFB1) gene is associated with chronic obstructive pulmonary disease (COPD). Human Molecular Genetics, 13(15), 1649-1656. doi:10.1093/hmg/ddh171 Christy, J., & Priyadharshini, L. (2018). Differential expression analysis of JAK/STAT pathway related genes in breast cancer. Meta Gene, 16, 122-129. doi:https://doi.org/10.1016/j.mgene.2018.02.008 Collingridge, G. L., & Bliss, T. V. P. (1987). NMDA receptors - their role in long-term potentiation. Trends in Neurosciences, 10(7), 288-293. doi:https://doi.org/10.1016/0166-2236(87)90175-5 Cunha, S. I., Bocci, M., Lövrot, J., Eleftheriou, N., Roswall, P., Cordero, E., Lindström, L., Bartoschek, M., Haller, B. K., Pearsall, R. S., Mulivor, A. W., Kumar, R., Larsson, C., Bergh, J., Pietras, K. (2015). Endothelial ALK1 Is a Therapeutic Target to Block Metastatic Dissemination of Breast Cancer. Cancer research, 75(12), 2445. Evangelou, M., Rendon, A., Ouwehand, W. H., Wernisch, L., & Dudbridge, F. (2012). Comparison of methods for competitive tests of pathway analysis. PLoS One, 7(7), 1-10. doi:10.1371/journal.pone.0041018 Fisher, R. A. (1932). Statistical Methods for Research Workers (4th edition ed.): London: Oliver and Boyd. Foltz, G., Yoon, J.-G., Lee, H., Ma, L., Tian, Q., Hood, L., & Madan, A. (2010). Epigenetic Regulation of Wnt Pathway Antagonists in Human Glioblastoma Multiforme. Genes & Cancer, 1(1), 81-90. doi:10.1177/1947601909356103 Gasco, M., Shami, S., & Crook, T. (2002). The p53 pathway in breast cancer. Breast Cancer Research, 4(2), 70. doi:10.1186/bcr426 Global Burden of Disease Cancer, C. (2017). Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 32 cancer groups, 1990 to 2015: A systematic analysis for the global burden of disease study. JAMA Oncology, 3(4), 524-548. doi:10.1001/jamaoncol.2016.5688 Goeman, J. J., van de Geer, S. A., de Kort, F., & van Houwelingen, H. C. (2004). A global test for groups of genes: testing association with a clinical outcome. Bioinformatics, 20(1), 93-99. doi:10.1093/bioinformatics/btg382 Haiman, C. A., Patterson, N., Freedman, M. L., Myers, S. R., Pike, M. C., Waliszewska, A., . . . Reich, D. (2007). Multiple regions within 8q24 independently affect risk for prostate cancer. Nature genetics, 39, 638. doi:10.1038/ng2015 https://www.nature.com/articles/ng2015#supplementary-information Hall, J. M., Friedman, L., Guenther, C., Lee, M. K., Weber, J. L., Black, D. M., & King, M. C. (1992). Closing in on a breast cancer gene on chromosome 17q. American journal of human genetics, 50(6), 1235-1242. Hanahan, D., & Weinberg, R. A. (2011). Hallmarks of cancer: the next generation. Cell, 144(5), 646-674. doi:10.1016/j.cell.2011.02.013 Harris, S. L., & Levine, A. J. (2005). The p53 pathway: positive and negative feedback loops. Oncogene, 24, 2899. doi:10.1038/sj.onc.1208615 Hattori, Y., Odagiri, H., Nakatani, H., Miyagawa, K., Naito, K., Sakamoto, H., Katoh, O., Yoshida, T., Sugimura, T., Terada, M. (1990). K-sam, an amplified gene in stomach cancer, is a member of the heparin-binding growth factor receptor genes. Proceedings of the National Academy of Sciences, 87(15), 5983. doi:10.1073/pnas.87.15.5983 Helgadottir, A., Manolescu, A., Thorleifsson, G., Gretarsdottir, S., Jonsdottir, H., Thorsteinsdottir, U., Samani, N. J., Gudmundsson,G., Grant, S. F. A., Thorgeirsson, G., Sveinbjornsdottir, S., Valdimarsson, E. M., Matthiasson, S. E., Johaansson, H., Gudmundsdottir, O.,Gurney, M. E., Sainz, J., Thorhallsdoottir, M., Andresdottir, M., Frigge, M. L., Topol, E. J., Kong, A., Gudnason, V., Hakonarson, H., Gulcher, J. R., Stefansson, K. (2004). The gene encoding 5-lipoxygenase activating protein confers risk of myocardial infarction and stroke. Nature genetics, 36, 233. doi:10.1038/ng1311 https://www.nature.com/articles/ng1311#supplementary-information Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2008). Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols, 4, 44. doi:10.1038/nprot.2008.211 https://www.nature.com/articles/nprot.2008.211#supplementary-information Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009). Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Research, 37(1), 1-13. doi:10.1093/nar/gkn923 Huelsken, J., & Behrens, J. (2002). The Wnt signalling pathway. Journal of Cell Science, 115(21), 3977. Hynes, N. E., & Boulay, A. (2006). The mTOR Pathway in Breast Cancer. Journal of Mammary Gland Biology and Neoplasia, 11(1), 53-61. doi:10.1007/s10911-006-9012-6 Jonsson, T., Stefansson, H., Steinberg, S., Jonsdottir, I., Jonsson, P. V., Snaedal, J., Bjornasson, S., Huttenlocher, J., Levey, A. I., Lah, J. J., Rujescu, D., Hampel, H., Giegling, I., Andreassen O. A., Engedal, K., Ulstein, I., Djurovic, S., Ibrahim-Verbaas, C., Hofman, A., Ikram, M. A., van Duijn, C. M., Thorsteinsdottir, U., Kong, A., Stefansson, K. (2012). Variant of TREM2 Associated with the Risk of Alzheimer's Disease. New England Journal of Medicine, 368(2), 107-116. doi:10.1056/NEJMoa1211103 Kounelakis, M. G., Zervakis, M. E., Giakos, G. C., & Kotsiakis, X. (2012, 2012//). The Role of the Glycolysis-Related Genes in Brain Gliomas Treatment Design. Paper presented at the 5th European Conference of the International Federation for Medical and Biological Engineering, Berlin, Heidelberg. Laplante, M., & Sabatini, D. M. (2009). mTOR signaling at a glance. Journal of Cell Science, 122(20), 3589. Lee, W. H., Shew, J. Y., Hong, F. D., Sery, T. W., Donoso, L. A., Young, L. J., Bookstein, R., Lee, E. Y. P. (1987). The retinoblastoma suceptibility gene encodes a nuclear phosphoprotein associated with DNA binding activity. Nature, 329(15), 642-645. Lin, S.-J., Lu, T.-P., Yu, Q.-Y., & Hsiao, C. K. (2018). Probabilistic prioritization of candidate pathway association with pathway score. BMC Bioinformatics, 19(1), 391. doi:10.1186/s12859-018-2411-z Lino, M., & Merlo, A. (2009). Translating biology into clinic: the case of glioblastoma. Current Opinion in Cell Biology, 21(2), 311-316. doi:https://doi.org/10.1016/j.ceb.2008.12.009 MacNeil, S. M., Johnson, W. E., Li, D. Y., Piccolo, S. R., & Bild, A. H. (2015). Inferring pathway dysregulation in cancers from multiple types of omic data. Genome Medicine, 7(1), 61. doi:10.1186/s13073-015-0189-4 Miki, Y., Swensen, J., Shattuck-Eidens, D., Futreal, P. A., Harshman, K., Tavtigian, S., Liu, Q., Cochran, C., Bennet, L. M., Ding, W., Bell, R., Rosenthal, J., Hussey, C., Tran, T., McMclure, M., Frye, C., Hattier, T., Phelps, R., Haugen-Strano, A., Katcher, H., Yakumo, K., Gholami, Z., Shaffer, D., Stone, S., Bayer, S., Wray, C., Bogden, R., Dayananth, P., Ward, J., Tonin, P., Narod, S., Bristow, P. K., Norris, F. H., Helvering, L., Morrison, P., Rostech, P., Lai, M., Barrett, J. C., Lewis, C., Neuhausen, S., Cannon-Albright, L., Goldgar, D., Wiseman, R., Kamb, A., Skolnick, M. H. (1994). A strong candidate for the breast and ovarian cancer susceptibility gene BRCA1. Science, 266(5182), 66. doi:10.1126/science.7545954 Pang, H., Lin, A., Holford, M., Enerson, B. E., Lu, B., Lawton, M. P., Floyd, E., Zhao, H. (2006). Pathway analysis using random forests classification and regression. Bioinformatics, 22(16), 2028-2036. doi:10.1093/bioinformatics/btl344 Parsons, D. W., Jones, S., Zhang, X., Lin, J. C.-H., Leary, R. J., Angenendt, P., Mankoo, P., Carter, H., Siu, I. M., Gallia, G. L., Olivi, A., McLendon, R., Rasheed, B. A., Keir, S., Nikolskaya, T., Nikolsky, Y., Busam, D. A., Tekleab, H., Diaz, L. A., Hartigan, J., Smith, D. R., Strausberg, R. L., Marie, S. K. N., Shinjo, S. M. O., Yan, H., Riggins, G. J., Bigner, D. D., Karchin, R., Papadopoulos, N., Parmigiani, G., Vogelstein, B., Velculescu, V. E., Kinzler, K. W. (2008). An Integrated Genomic Analysis of Human Glioblastoma Multiforme. Science. doi:10.1126/science.1164382 Pociot, F., & Lernmark, Å. (2016). Genetic risk factors for type 1 diabetes. The Lancet, 387(10035), 2331-2339. doi:https://doi.org/10.1016/S0140-6736(16)30582-7 Purves, W. K., Purves, W. K., Orians, G. H., Sadava, D., & Heller, H. C. (2003). Life: the science of biology: volume III: plants and animals (Vol. 3): Macmillan. Rawlings, J. S., Rosler, K. M., & Harrison, D. A. (2004). The JAK/STAT signaling pathway. Journal of Cell Science, 117(8), 1281. Rubin, R., Strayer, D. S., & Rubin, E. (2008). Rubin's pathology: clinicopathologic foundations of medicine: Lippincott Williams & Wilkins. Shen, C., Beroukhim, R., Schumacher, S., Zhou, J., Chang, M., Signoretti, S., & Kaelin, W. (2011). Genetic and Functional Studies Implicate HIF1α as a 14q Kidney Cancer Suppressor Gene. Cancer Discovery, CD-11-0098. doi:10.1158/2159-8290.CD-11-0098 Sinn, H.-P., & Kreipe, H. (2013). A Brief Overview of the WHO Classification of Breast Tumors, 4th Edition, Focusing on Issues and Updates from the 3rd Edition. Breast Care, 8(2), 149-154. doi:10.1159/000350774 Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., . . . Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences of the United States of America, 102(43), 15545-15550. doi:10.1073/pnas.0506580102 Tarca, A. L., Draghici, S., Khatri, P., Hassan, S. S., Mittal, P., Kim, J.-s., Kim, C. J., Kusanovic, J. P., Romero, R. (2009). A novel signaling pathway impact analysis. Bioinformatics, 25(1), 75-82. doi:10.1093/bioinformatics/btn577 Welch, H. G., Woloshin, S., & Schwartz, L. M. (2008). The sea of uncertainty surrounding ductal carcinoma in situ—the price of screening mammography: Oxford University Press. Wu, D., Han, B., Guo, L., & Fan, Z. (2016). Molecular mechanisms associated with breast cancer based on integrated gene expression profiling by bioinformatics analysis. Journal of Obstetrics and Gynaecology, 36(5), 615-621. doi:10.3109/01443615.2015.1127902 Yang, I. A., Holloway, J. W., & Fong, K. M. (2013). Genetic susceptibility to lung cancer and co-morbidities. Journal of Thoracic Disease, S454-S462. Zhang, B., Kirov, S., & Snoddy, J. (2005). WebGestalt: an integrated system for exploring gene sets in various biological contexts. Nucleic Acids Research, 33(Web Server issue), W741-W748. doi:10.1093/nar/gki475 Zhang, G., & Ghosh, S. (2001). Toll-like receptor–mediated NF-κB activation: a phylogenetically conserved paradigm in innate immunity. The Journal of Clinical Investigation, 107(1), 13-19. doi:10.1172/JCI11837 Zhou, Z., Qiao, J. X., Shetty, A., Wu, G., Huang, Y., Davidson, N. E., & Wan, Y. (2014). Regulation of estrogen receptor signaling in breast carcinogenesis and breast cancer therapy. Cellular and molecular life sciences : CMLS, 71(8), 1549-1549. doi:10.1007/s00018-013-1376-3 顱內腫瘤研究委員會, T. (2004). 腦瘤之診斷與治療共識: 國家衛生研究院 (NHRI). | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/7386 | - |
| dc.description.abstract | 疾病與基因之間的關係密切，以十大死因而言，有九項是疾病。另外，癌症是目前全球關心的重要議題，不僅每年罹癌人數多，且因癌症而死亡人數亦高，而癌症的發生亦與基因相關。健康者進行基因檢測能進一步了解自身的疾病風險，進而預防疾病發生，而病患能透過與疾病相關之關鍵基因進行修復或破壞，進而治疾療疾病。因此，許多研究人員關心與疾病有關之關鍵基因，但人類基因超過兩萬個，研究人員需耗費大量的時間與金錢才能找到關鍵基因，為了降低成本，提供有優先順序的清單可能有助於尋找關鍵基因。目前篩選與疾病相關基因的方法大約分成三種，如單一基因方法(Singal marker test)、基因群方法(Gene set analysis)及生物路徑方法(Pathway analysis)，提供候選基因或候選基因集合。
部份的統計方法從單一基因尋找與疾病相關之基因，像是T檢定(T test)或是卡方檢定(Chi-square test)；然而因標誌基因(Biomarker)數多，通常有多重檢定的問題，且沒有考慮基因之間的關係。基因不會單獨運作，需與其他基因共同影響一個生物過程(Biological process)。因此，比起只關心單一基因對生物過程的影響，更應該考慮一群基因整生物過程的影響。於是有第二類型的方法，以基因群方法尋找與疾病相關之基因，像是Gene Set Enrichment Analysis (GSEA)、Over Representation Analysis (ORA)、Globa test及Fisher’s method，基因事先被分成許多基因集合(Gene sets)，而找到的基因集合可能是一個好的指標，但是在生物意義上卻很難解釋。生物路徑為一群基因共同影響一個生物過程。第三種方法以生物路徑尋找相關之基因，例如Signaling Pathway Impact Analysis (SPIA)，目前此類方法沒有考慮生物路徑間的競爭及關係，有些亦沒有考慮生物路徑中基因間的關係，故本就究計劃提出新方法克服目前現有方法的限制。 本研究不僅考慮生物路徑中基因間的關係，並且同時考慮多個生物路徑間互相競爭，又能處理生物路徑間的關係。在上述的條件下提出的新方法Bayesian Approach to Prioritizing Pathway (BAPP)，BAPP提供與疾病相關之有序候選生物路徑清單，更進一步於首要生物路徑中尋找關鍵基因，提供另一個與疾病相關之有序關鍵基因清單，並且結合常見生物路徑資料庫Kyoto Encyclopedia of Genes and Genomes (KEGG)於本研究中。BAPP在模擬中有很好的表現。不論是排序候選生物路徑，或是關鍵基因，BAPP皆能控制型一誤差在0.05下。BAPP正確排序候選生物路徑之正確率比他方法高，並且能找到真正的關鍵基因。 本研究將提出的新方法應用於乳癌(Breast cancer)及膠質母細胞瘤(Glioblastoma multiforme)資料。在乳癌資料中，BAPP找出乳癌之首要生物路徑為Jak-STAT生物路徑，進一步從此生物路徑找出37個關鍵基因，而不曾被提及與乳癌有關之味覺傳導(Taste transduction)生物路徑被BAPP排在最後面。在膠質母細胞瘤資料中，本研究的方法找出Long-term potentiation為膠質母細胞瘤之首要生物路徑，從中找出4個關鍵基因。 | zh_TW |
| dc.description.abstract | Cancer is an important topic of global concern. Some cancers are closely related to genetic aberrations. Not only is there a large number of new cancer patients per year, but the number of deaths due to cancer is also high. Genetic testing can help understand one’s disease risk and may prevent disease occurrence, if the causal key genes can be identified. Therefore, many researchers focus on identifying the key genes, among more than 20,000 human genes. Researchers need a lot of time and money to find key genes. In order to reduce costs, providing a prioritized list may help to find key genes. Currently, methods for screening genes associated with diseases are roughly classified into three types, such as the single marker tests, gene-set analysis methods, and pathway analysis, to provide candidate genes or candidate gene sets.
Some statistical methods find disease-related genes from a single marker test, such as the T test or Chi-square test. However, due to the large number of biomarkers, scientists need to face the issue of multiple testing. Single marker test did not consider the relationship between genes. Genes do not work alone and need to work with other genes to affect a biological process. Therefore, rather than focusing on the effect of a single gene on biological processes, the effect of a group of genes should be considered. So there is a second type of method to find disease-related genes in a group of genes, such as the Gene Set Enrichment Analysis (GSEA), Over Representation Analysis (ORA), Globa test, and Fisher's method. A pathway is a collection of genes containing biological meaning. The pathway represents a biological process carried out by a group of genes. The third method uses pathways to find related genes, such as Signaling Pathway Impact Analysis (SPIA). Currently, such methods do not consider simultaneously several competing pathways; they do not incorporate the relationship between pathways, nor account for the relationship between genes. This study will provide a novel method to overcome the limitations of current methods. This study not only considers the relationship between genes in the pathway but also considers the competition between several pathways and the relationship between pathways. Bayesian Approach to Prioritizing Pathway (BAPP), a novel method proposed under the above conditions, provides a list of ordered candidate pathways associated with the disease. The BAPP can further search for key genes in the primary pathway, and provide disease-related key genes. BAPP can be applied on the common pathway database, Kyoto Encyclopedia of Genes and Genomes (KEGG). Simulations show that BAPP performs well. Whether it is prioritizing candidate pathways or key genes, BAPP can control the type I error rate under 0.05. BAPP correctly ranks candidate pathways at a higher accuracy than other methods and can find true key genes. This novel method is applied to a breast cancer study and a glioblastoma multiforme study. In breast cancer data, BAPP identifies the primary pathway of breast cancer as the Jak-STAT signaling pathway, and further identifies 37 key genes in this pathway. The Taste transduction pathway that has not been reported to associate with breast cancer is ranked last by BAPP. In the glioblastoma multiforme study, BAPP identifies Long-term potentiation as the primary pathway, and from which four key genes are identified. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-19T17:42:38Z (GMT). No. of bitstreams: 1 ntu-108-F01849030-1.pdf: 6676654 bytes, checksum: c1360b68155a5461d33f24c669e3094b (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 目錄
誌謝 i 摘要 ii Abstract iv 目錄 vi 圖目錄 ix 表目錄 xiv 第一章 背景與動機 1 第一節 單一基因方法 2 第二節 基因群方法 2 第三節 生物路徑方法 7 第四節 研究目的與動機 9 第一項 排序之基因表現量 10 第二項 基因間正負相關 11 第二章 研究方法 14 第一節 尋找首要生物路徑 14 第二節 尋找關鍵基因 17 第三節 處理生物路徑資料庫 18 第三章 模擬 21 第一節 模擬資料來源 21 第二節 尋找首要生物路徑之模擬 22 第一項 型一誤差之模擬 22 第二項 正確率之模擬 25 第三節 尋找關鍵基因之模擬 33 第一項 型一誤差之模擬 33 第二項 正確分辨之模擬 35 第四章 實例探討 50 第一節 乳腺管原位癌(Ductal carcinoma in situ, DCIS) 50 第一項 基因表現量資料來源 50 第二項 生物路徑資料來源 51 第三項 結果 52 第二節 膠質母細胞瘤 69 第一項 基因表現量資料來源 69 第二項 生物路徑資料來源 70 第三項 結果 70 第五章 討論與後續研究 84 Reference 87 附錄一 KEGG資料庫的生物路徑資訊轉換與BAPP方法所需之生物路徑基因 92 附錄二 BAPP方法 94 附錄三 Long-term potentiation、MAPK、Glioma及WNT生物路徑之生物路徑大小及其顯著基因數 98 附錄四 p53、Estrogen、Jak-STAT、mTOR、Oocyte meiosis及味覺傳導生物路徑之基因表現量熱點圖 99 附錄五 p53、Estrogen、Jak-STAT、mTOR、Oocyte meiosis及味覺傳導生物路徑之基因表現量順序熱點圖 105 附錄六 Jak-STAT生物路徑中之關鍵基因 111 附錄七 Long-term potentiation、MAPK、Glioma及WNT生物路徑之基因表現量熱點圖，青綠色為控制組，洋紅色為實驗組 113 附錄八 Long-term potentiation、MAPK、Glioma及WNT生物路徑之基因表現量順序熱點圖，青綠色為控制組，洋紅色為實驗組 117 附錄九 各標準選擇參考基因以貝氏後驗機率評估穩定性 121 附錄十 正確分辨關鍵基因模擬的詳細結果 127 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 生物路徑分數 | zh_TW |
| dc.subject | 生物路徑資料庫 | zh_TW |
| dc.subject | 關鍵基因 | zh_TW |
| dc.subject | 排序生物路徑 | zh_TW |
| dc.subject | 貝氏方法 | zh_TW |
| dc.subject | pathway database | en |
| dc.subject | key genes | en |
| dc.subject | prioritizing pathways | en |
| dc.subject | Bayesian method | en |
| dc.subject | pathway score | en |
| dc.title | 以生物路徑分數及貝氏方法量化與排序生物路徑及基因相關性之分析 | zh_TW |
| dc.title | Use of Pathway Score in Bayesian Model to Quantify and Prioritize Pathway Association and Gene Ranking | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 107-1 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.coadvisor | 盧子彬 | zh_TW |
| dc.contributor.coadvisor | ; | en |
| dc.contributor.oralexamcommittee | 蔡懷寬;楊欣洲;陳為堅;林菀俞 | zh_TW |
| dc.contributor.oralexamcommittee | ;;; | en |
| dc.subject.keyword | 生物路徑分數,貝氏方法,排序生物路徑,關鍵基因,生物路徑資料庫, | zh_TW |
| dc.subject.keyword | pathway score,Bayesian method,prioritizing pathways,key genes,pathway database, | en |
| dc.relation.page | 173 | - |
| dc.identifier.doi | 10.6342/NTU201900358 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2019-02-12 | - |
| dc.contributor.author-college | 公共衛生學院 | - |
| dc.contributor.author-dept | 流行病學與預防醫學研究所 | - |
| dc.date.embargo-lift | 2024-03-05 | - |
| 顯示於系所單位： | 流行病學與預防醫學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-107-1.pdf 未授權公開取用 | 6.52 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。