請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/30496完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳中明(Chung-Ming Chen) | |
| dc.contributor.author | Yun-Ju Sun | en |
| dc.contributor.author | 孫韻如 | zh_TW |
| dc.date.accessioned | 2021-06-13T02:05:23Z | - |
| dc.date.available | 2012-08-08 | |
| dc.date.copyright | 2011-08-08 | |
| dc.date.issued | 2011 | |
| dc.date.submitted | 2011-08-02 | |
| dc.identifier.citation | 1. Volinia, S., et al., A microRNA expression signature of human solid tumors defines cancer gene targets. Proc Natl Acad Sci U S A, 2006. 103(7): p. 2257-61.
2. Ofir, M., D. Hacohen, and D. Ginsberg, miR-15 and miR-16 Are direct transcriptional targets of E2F1 that limit E2F-induced proliferation by targeting cyclin E. Mol Cancer Res, 2011. 9(4): p. 440-7. 3. Garzon, R., G. Marcucci, and C.M. Croce, Targeting microRNAs in cancer: rationale, strategies and challenges. Nat Rev Drug Discov, 2010. 9(10): p. 775-89. 4. Chen, K. and N. Rajewsky, The evolution of gene regulation by transcription factors and microRNAs. Nat Rev Genet, 2007. 8(2): p. 93-103. 5. Bartel, D.P., MicroRNAs: genomics, biogenesis, mechanism, and function. Cell, 2004. 116(2): p. 281-97. 6. Lim, L.P., et al., Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature, 2005. 433(7027): p. 769-73. 7. Berezikov, E., et al., Mammalian mirtron genes. Mol Cell, 2007. 28(2): p. 328-36. 8. Vasudevan, S., Y. Tong, and J.A. Steitz, Switching from repression to activation: microRNAs can up-regulate translation. Science, 2007. 318(5858): p. 1931-4. 9. Mani, R., et al., Defining genetic interaction. Proc Natl Acad Sci U S A, 2008. 105(9): p. 3461-6. 10. Chuang, C.L., et al., Uncovering transcriptional interactions via an adaptive fuzzy logic approach. BMC Bioinformatics, 2009. 10: p. 400. 11. Fazi, F., et al., A minicircuitry comprised of microRNA-223 and transcription factors NFI-A and C/EBPalpha regulates human granulopoiesis. Cell, 2005. 123(5): p. 819-31. 12. Wang, J., et al., TransmiR: a transcription factor-microRNA regulation database. Nucleic Acids Res, 2010. 38(Database issue): p. D119-22. 13. Alexiou, P., et al., miRGen 2.0: a database of microRNA genomic information and regulation. Nucleic Acids Res, 2010. 38(Database issue): p. D137-41. 14. Creighton, C.J., et al., A bioinformatics tool for linking gene expression profiling results with public databases of microRNA target predictions. RNA, 2008. 14(11): p. 2290-6. 15. Rajewsky, N., microRNA target predictions in animals. Nat Genet, 2006. 38 Suppl: p. S8-13. 16. Dai, Y. and X. Zhou, Computational methods for the identification of microRNA targets. Open Access Bioinformatics, 2010. 2: p. 29-39. 17. Lewis, B.P., C.B. Burge, and D.P. Bartel, Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell, 2005. 120(1): p. 15-20. 18. Lewis, B.P., et al., Prediction of mammalian microRNA targets. Cell, 2003. 115(7): p. 787-98. 19. John, B., et al., Human MicroRNA targets. PLoS Biol, 2004. 2(11): p. e363. 20. Krek, A., et al., Combinatorial microRNA target predictions. Nat Genet, 2005. 37(5): p. 495-500. 21. Rehmsmeier, M., et al., Fast and effective prediction of microRNA/target duplexes. RNA, 2004. 10(10): p. 1507-17. 22. Thadani, R. and M.T. Tammi, MicroTar: predicting microRNA targets from RNA duplexes. BMC Bioinformatics, 2006. 7 Suppl 5: p. S20. 23. Huang, J.C., et al., Using expression profiling data to identify human microRNA targets. Nat Methods, 2007. 4(12): p. 1045-9. 24. Hobert, O., Gene regulation by transcription factors and microRNAs. Science, 2008. 319(5871): p. 1785-6. 25. Tran, D.H., et al., Computational discovery of miR-TF regulatory modules in human genome. Bioinformation, 2010. 4(8): p. 371-7. 26. Zhou, Y., et al., Inter- and intra-combinatorial regulation by transcription factors and microRNAs. BMC Genomics, 2007. 8: p. 396. 27. Friard, O., et al., CircuitsDB: a database of mixed microRNA/transcription factor feed-forward regulatory circuits in human and mouse. BMC Bioinformatics, 2010. 11: p. 435. 28. Le Bechec, A., et al., MIR@NT@N: a framework integrating transcription factors, microRNAs and their targets to identify sub-network motifs in a meta-regulation network model. BMC Bioinformatics, 2011. 12: p. 67. 29. Wang, G., et al., Transcription factor and microRNA regulation in androgen-dependent and -independent prostate cancer cells. BMC Genomics, 2008. 9 Suppl 2: p. S22. 30. Chen, C.Y., et al., Coregulation of transcription factors and microRNAs in human transcriptional regulatory network. BMC Bioinformatics, 2011. 12 Suppl 1: p. S41. 31. Krutzfeldt, J., et al., Silencing of microRNAs in vivo with 'antagomirs'. Nature, 2005. 438(7068): p. 685-9. 32. Choi, W.Y., A.J. Giraldez, and A.F. Schier, Target protectors reveal dampening and balancing of Nodal agonist and antagonist by miR-430. Science, 2007. 318(5848): p. 271-4. 33. Lin, P.Y., S.L. Yu, and P.C. Yang, MicroRNA in lung cancer. Br J Cancer, 2010. 103(8): p. 1144-8. 34. Calin, G.A., et al., Human microRNA genes are frequently located at fragile sites and genomic regions involved in cancers. Proc Natl Acad Sci U S A, 2004. 101(9): p. 2999-3004. 35. Johnson, C.D., et al., The let-7 microRNA represses cell proliferation pathways in human cells. Cancer Res, 2007. 67(16): p. 7713-22. 36. Chin, L.J., et al., A SNP in a let-7 microRNA complementary site in the KRAS 3' untranslated region increases non-small cell lung cancer risk. Cancer Res, 2008. 68(20): p. 8535-40. 37. Hayashita, Y., et al., A polycistronic microRNA cluster, miR-17-92, is overexpressed in human lung cancers and enhances cell proliferation. Cancer Res, 2005. 65(21): p. 9628-32. 38. Ebi, H., et al., Counterbalance between RB inactivation and miR-17-92 overexpression in reactive oxygen species and DNA damage induction in lung cancers. Oncogene, 2009. 28(38): p. 3371-9. 39. Liu, X., et al., MicroRNA-31 functions as an oncogenic microRNA in mouse and human lung cancer cells by repressing specific tumor suppressors. J Clin Invest, 2010. 120(4): p. 1298-309. 40. Galluzzi, L., et al., miR-181a and miR-630 regulate cisplatin-induced cancer cell death. Cancer Res, 2010. 70(5): p. 1793-803. 41. Rosell, R., et al., Screening for epidermal growth factor receptor mutations in lung cancer. N Engl J Med, 2009. 361(10): p. 958-67. 42. Weiss, G.J., et al., EGFR regulation by microRNA in lung cancer: correlation with clinical response and survival to gefitinib and EGFR expression in cell lines. Ann Oncol, 2008. 19(6): p. 1053-9. 43. Cho, W.C., A.S. Chow, and J.S. Au, Restoration of tumour suppressor hsa-miR-145 inhibits cancer cell growth in lung adenocarcinoma patients with epidermal growth factor receptor mutation. Eur J Cancer, 2009. 45(12): p. 2197-206. 44. Webster, R.J., et al., Regulation of epidermal growth factor receptor signaling in human cancer cells by microRNA-7. J Biol Chem, 2009. 284(9): p. 5731-41. 45. Seike, M., et al., MiR-21 is an EGFR-regulated anti-apoptotic factor in lung cancer in never-smokers. Proc Natl Acad Sci U S A, 2009. 106(29): p. 12085-90. 46. Schaefer, U., et al., TRAIL: a multifunctional cytokine. Front Biosci, 2007. 12: p. 3813-24. 47. Garofalo, M., et al., miR-221&222 regulate TRAIL resistance and enhance tumorigenicity through PTEN and TIMP3 downregulation. Cancer Cell, 2009. 16(6): p. 498-509. 48. Lebanony, D., et al., Diagnostic assay based on hsa-miR-205 expression distinguishes squamous from nonsquamous non-small-cell lung carcinoma. J Clin Oncol, 2009. 27(12): p. 2030-7. 49. Landi, M.T., et al., MicroRNA expression differentiates histology and predicts survival of lung cancer. Clin Cancer Res, 2010. 16(2): p. 430-41. 50. Yanaihara, N., et al., Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell, 2006. 9(3): p. 189-98. 51. Yu, S.L., et al., MicroRNA signature predicts survival and relapse in lung cancer. Cancer Cell, 2008. 13(1): p. 48-57. 52. Raponi, M., et al., MicroRNA classifiers for predicting prognosis of squamous cell lung cancer. Cancer Res, 2009. 69(14): p. 5776-83. 53. Hu, Z., et al., Serum microRNA signatures identified in a genome-wide serum microRNA expression profiling predict survival of non-small-cell lung cancer. J Clin Oncol, 2010. 28(10): p. 1721-6. 54. Lu, J., et al., MicroRNA expression profiles classify human cancers. Nature, 2005. 435(7043): p. 834-8. 55. Ramaswamy, S., et al., Multiclass cancer diagnosis using tumor gene expression signatures. Proc Natl Acad Sci U S A, 2001. 98(26): p. 15149-54. 56. Landi, M.T., et al., Gene expression signature of cigarette smoking and its role in lung adenocarcinoma development and survival. PLoS One, 2008. 3(2): p. e1651. 57. Hou, J., et al., Gene expression-based classification of non-small cell lung carcinomas and survival prediction. PLoS One, 2010. 5(4): p. e10312. 58. Stearman, R.S., et al., Analysis of orthologous gene expression between human pulmonary adenocarcinoma and a carcinogen-induced murine model. Am J Pathol, 2005. 167(6): p. 1763-75. 59. Jiang, C., et al., TRED: a transcriptional regulatory element database, new entries and other development. Nucleic Acids Res, 2007. 35(Database issue): p. D137-40. 60. Grimson, A., et al., MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol Cell, 2007. 27(1): p. 91-105. 61. Enright, A.J., et al., MicroRNA targets in Drosophila. Genome Biol, 2003. 5(1): p. R1. 62. Liu, Q., et al., miR-16 family induces cell cycle arrest by regulating multiple cell cycle genes. Nucleic Acids Res, 2008. 36(16): p. 5391-404. 63. Takahashi, Y., et al., MiR-107 and MiR-185 can induce cell cycle arrest in human non small cell lung cancer cell lines. PLoS One, 2009. 4(8): p. e6677. 64. Loeb, D.M., et al., Cyclin E is a target of WT1 transcriptional repression. J Biol Chem, 2002. 277(22): p. 19627-32. 65. Rustighi, A., et al., A polypyrimidine/polypurine tract within the Hmga2 minimal promoter: a common feature of many growth-related genes. Biochemistry, 2002. 41(4): p. 1229-40. 66. Bruchova, H., M. Merkerova, and J.T. Prchal, Aberrant expression of microRNA in polycythemia vera. Haematologica, 2008. 93(7): p. 1009-16. 67. Ji, Q., et al., Restoration of tumor suppressor miR-34 inhibits human p53-mutant gastric cancer tumorspheres. BMC Cancer, 2008. 8: p. 266. 68. Scott, G.K., et al., Coordinate suppression of ERBB2 and ERBB3 by enforced expression of micro-RNA miR-125a or miR-125b. J Biol Chem, 2007. 282(2): p. 1479-86. 69. Hsieh, J.K., et al., Novel function of the cyclin A binding site of E2F in regulating p53-induced apoptosis in response to DNA damage. Mol Cell Biol, 2002. 22(1): p. 78-93. 70. Diaz, R., et al., Deregulated expression of miR-106a predicts survival in human colon cancer patients. Genes Chromosomes Cancer, 2008. 47(9): p. 794-802. 71. Ivanovska, I., et al., MicroRNAs in the miR-106b family regulate p21/CDKN1A and promote cell cycle progression. Mol Cell Biol, 2008. 28(7): p. 2167-74. 72. Fontana, L., et al., Antagomir-17-5p abolishes the growth of therapy-resistant neuroblastoma through p21 and BIM. PLoS One, 2008. 3(5): p. e2236. 73. Qi, J., et al., microRNAs regulate human embryonic stem cell division. Cell Cycle, 2009. 8(22): p. 3729-41. 74. Song, B., et al., MicroRNA-21 regulates breast cancer invasion partly by targeting tissue inhibitor of metalloproteinase 3 expression. J Exp Clin Cancer Res, 2010. 29: p. 29. 75. Gabriely, G., et al., MicroRNA 21 promotes glioma invasion by targeting matrix metalloproteinase regulators. Mol Cell Biol, 2008. 28(17): p. 5369-80. 76. Ahonen, M., A.H. Baker, and V.M. Kahari, Adenovirus-mediated gene delivery of tissue inhibitor of metalloproteinases-3 inhibits invasion and induces apoptosis in melanoma cells. Cancer Res, 1998. 58(11): p. 2310-5. 77. Hallstrom, T.C. and J.R. Nevins, Balancing the decision of cell proliferation and cell fate. Cell Cycle, 2009. 8(4): p. 532-5. 78. Koutsami, M.K., et al., Centrosome abnormalities are frequently observed in non-small-cell lung cancer and are associated with aneuploidy and cyclin E overexpression. J Pathol, 2006. 209(4): p. 512-21. 79. Watabe, Y., et al., Aryl hydrocarbon receptor functions as a potent coactivator of E2F1-dependent trascription activity. Biol Pharm Bull, 2010. 33(3): p. 389-97. 80. Ma, Y., et al., Transgenic cyclin E triggers dysplasia and multiple pulmonary adenocarcinomas. Proc Natl Acad Sci U S A, 2007. 104(10): p. 4089-94. 81. Bandi, N., et al., miR-15a and miR-16 are implicated in cell cycle regulation in a Rb-dependent manner and are frequently deleted or down-regulated in non-small cell lung cancer. Cancer Res, 2009. 69(13): p. 5553-9. 82. Peltier, H.J. and G.J. Latham, Normalization of microRNA expression levels in quantitative RT-PCR assays: identification of suitable reference RNA targets in normal and cancerous human solid tissues. RNA, 2008. 14(5): p. 844-52. 83. Ding, L., et al., Somatic mutations affect key pathways in lung adenocarcinoma. Nature, 2008. 455(7216): p. 1069-75. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/30496 | - |
| dc.description.abstract | 本研究的目的在探討在肺腺癌 (lung adenocarcinoma, ADC)中哪些轉錄因子 (transcriptional factors, TFs)、微小核糖核酸 (microRNAs, miRNAs)與基因間會發生調控關係，並預測可能影響肺腺癌發生的TF-miRNA共同調控 (co-regulated)目標基因關係。
在基因調控方面，已知TF對目標基因扮演主要的轉錄調控(transcriptional regulation)角色，miRNA在轉錄後扮演微調的角色。MicroRNAs是一群約22nt~28nt短的非編碼RNAs (non-coding RNAs)，透過結合上它們的目標基因 (target genes)達到轉錄後調控 (post-transcriptional regulation)。研究顯示許多miRNAs會參與癌症，由分析差異表現得知對不同種類的癌症組織有不同的miRNAs參與調控，並可以分成分別抑制致癌基因 (oncogenes)及腫瘤抑制基因 (tumor suppressor genes)的tumor suppressor miRNAs 及oncomirs。癌症中長久以來以肺癌 (lung cancer)位居癌症死亡率第一，其中80%的病人為非小細胞肺癌 (non-small cell lung cancer, NSCLC)，又以肺腺癌為大宗。目前對TF調控基因方面已有眾多研究，但miRNA如何參與調控並不是很了解。為了更了解肺腺癌的形成原因以及TFs與miRNAs在癌症中的功能，因此探討TF與miRNA在肺腺癌中如何共同調控目標基因。 本研究建構了一個兩階段的模糊模型 (two-stage fuzzy model)來模擬TF與miRNA調控目標基因的過程，並預測在肺腺癌中TF與miRNA合作調控目標基因的關係。第一階段模擬TF結合到目標基因，擷取序列資訊用AdaFuzzy演算法做學習並預測TF對目標基因的轉錄調控，第二階段模擬第一階段所預測的目標基因被TF調控後是否受到miRNA轉錄後調控而形成共同調控。在第二階段除了序列資訊還加入肺腺癌的微陣列 (microarray)基因表現量，結合預測軟體TargetScan及miRanda計算分數來學習預測miRNA與基因的結合強度以及第一階段預測TF與基因的結合強度使用模糊推論系統 (fuzzy inference system, FIS)來預測TF-miRNA共同調控的關係。 結果顯示，所預測出來的共同調控關係中，包含一個已由實驗證實在肺腺癌中有調控的三者關係，E2F─Cyclin E─miR-15。透過分析細胞週期 (cell cycle)癌症途徑中Cyclin E的上游基因不顯著表現推論此關係可能參與肺腺癌的形成。進一步分析分數相近或高於已知三者關係可預測出可能透過共同調控影響肺腺癌的TFs與miRNAs，包含與cell cycle有關的E2F─Cyclin E─miR-15ab/195/185/107以及與p53 signaling pathway有關的p53─TIMP3─miR-21。由結果推測，肺腺癌的形成可能受到這些TF-miRNA合作調控的影響。透過此方法使我們更了解肺癌的形成原因與miRNAs扮演的功能，以及miRNAs如何參與癌症途徑。另一方面，對於癌症的治療，共同調控的TF-miRNA可被來設計來更有效率殺死癌細胞。 | zh_TW |
| dc.description.abstract | The purpose of this study is to uncover the relations of certain transcription factors (TFs) and microRNAs (miRNAs) regulating their target genes seperately using genomics data, and to further predict the co-regulation of TF and miRNA on their target genes, which may be involved in the lung adenocarcinoma (ADC) in human.
In terms of gene regulation, TFs are known to play a major role on regulating their targets (TF-targets), while miRNAs play a fine-tuning role at the targets' post-transcriptional levels (miRNA-targets). MicroRNAs are a group of short and 22nt ~ 28nt non-coding RNAs, which bind to their targets' 3'UTR sites to carry out post-transcriptional regulation. Previous studies have shown that many miRNAs may be involved in cancers. By analyzing the differential expressions of miRNAs, different miRNAs were uncovered to be involved in different kinds of cancer. MicroRNA could be classified into tumor suppressor miRNAs or oncomirs by their functions of inhibiting oncogenes or tumor suppressor genes. The death rate of lung cancer has long been ranked the top one among all cancers in Taiwan, of which 80% of patients are non-small cell lung cancer (NSCLC), in which the adenocarcinoma subtype is the most common. There many of studies on TF-gene regulations, but how miRNAs are involved in the regulation of lung ADC is not well understood. This study proposes a model to infer which TF and which miRNA co-regulate their target genes in lung ADC. A two-stage fuzzy method was constructed to model the processes of TF and miRNA regulating targets sequentially using genomics data, and a fuzzy inference system (FIS) was used to infer the co-regulation of TFs and miRNAs. The first stage modeled the process of TFs' binding to their targets. We first extracted DNA sequence data and inputed them into AdaFuzzy which learned and infered the TF-target regulation. In the second stage, AdaFuzzy and FIS modeled whithin a target gene in a predicted TF-target pair was further regulated by a miRNA, thus this triple had a co-regulation relation. In addition to sequence information, microarray gene expression data (MGED) containing lung ADC tissues and normal lung tissues was also used in the FIS. Integrating MGED, the TF-target binding strength predicted in the first stage and the miRNA-target relation strength as inputs, the FIS could infer the co-ragulations resonably. The miRNA-target were predicted by AdaFuzzy which learned binding strength scores from miRanda and TargetScan. The results show that the predicted co-regulations include a validated triple E2F─Cyclin E─miR-15 which has been confirmed by lung ADC cell lines. Because the upstream genes of cyclin E were not significantly expressed in the cell cycle pathway, and both E2F and miR-15 differentially expressed, we infered that this triple may be involved in the tumorigenesis of lung ADC. Candidate co-regulation of TF-miRNAs, which consisted of cell cycle-related E2F─Cyclin E─miR-15ab/195 /185/107 and p53 signaling pathway-related p53─TIMP3─miR-21 were further predicted from the triples having similar or higher score of the valified triple. These results are attempts to depict the roles of miRNAs play and their co-regulations in lung ADC. On the other hand, a potential therapy may be designed via the relationship of TF-miRNA co-regulation to kill cancer cells efficiently. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T02:05:23Z (GMT). No. of bitstreams: 1 ntu-100-R98548029-1.pdf: 1819676 bytes, checksum: a48e176097fe339c98b7bd8dd58e5468 (MD5) Previous issue date: 2011 | en |
| dc.description.tableofcontents | 口試委員審定書
誌 謝 i 摘 要 ii Abstract iv 目 錄 vi 圖 目 錄 viii 表 目 錄 x 第一章 導論 1 1.1 研究目的 1 1.2 研究動機 1 1.3 研究方法 2 1.4 研究結果與討論 4 第二章 文獻探討 5 2.1 MicroRNA介紹 5 2.2 肺癌介紹 (lung cancer) 9 2.3 調控網路Regulatory network 11 2.4 透過miRNA作為癌症治療劑 (miRNA-based therapies) 17 第三章 研究方法 26 3.1 資料分析 (data analysis) 26 3.2 差異表現基因 (differentially expressed genes) 31 3.3 轉錄調控的預測 (prediction of transcriptional regulation) 34 3.4 轉錄後調控的預測 (prediction of post-transcriptional regulation) 43 3.5 TF-miRNA共同調控的預測 (prediction of TF-miRNA co- regulation) 48 第四章 研究結果 51 4.1 肺腺癌中轉錄調控與轉錄後調控分別的預測結果 51 4.2 肺腺癌中TF-miRNA共同調控目標基因的預測結果 53 4.3 TF-miRNA共同調控與肺腺癌發生途徑的關係 63 第五章 討論與結論 74 5.1 比較加入表現量資訊前後的效果 74 5.2 各別預測模型與結合預測模型 77 5.3 兩組表現量資訊預測結果的比較 79 5.4 預測結果與肺腺癌的關係 80 5.5 結論 83 參考文獻 85 | |
| dc.language.iso | zh-TW | |
| dc.subject | 轉錄後調控 | zh_TW |
| dc.subject | 微小核糖核酸 | zh_TW |
| dc.subject | 肺腺癌 | zh_TW |
| dc.subject | 共同調控 | zh_TW |
| dc.subject | 模糊推論系統 | zh_TW |
| dc.subject | lung adenocarcinoma | en |
| dc.subject | post-transcriptional regulation | en |
| dc.subject | co-regulation | en |
| dc.subject | fuzzy inference system | en |
| dc.subject | microRNAs | en |
| dc.title | 探討轉錄因子與微小核醣核酸的共同調控與肺腺癌的關係 | zh_TW |
| dc.title | The Co-regulations of Transcription Factors and MicroRNAs in Lung Adenocarcinoma | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 99-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 謝叔蓉(Grace Shwu-Rong Shieh) | |
| dc.contributor.oralexamcommittee | 白果能(Konan Peck),陳惠文(Huei-Wen Chen) | |
| dc.subject.keyword | 微小核糖核酸,肺腺癌,轉錄後調控,共同調控,模糊推論系統, | zh_TW |
| dc.subject.keyword | microRNAs,lung adenocarcinoma,post-transcriptional regulation,co-regulation,fuzzy inference system, | en |
| dc.relation.page | 90 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2011-08-02 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 醫學工程學研究所 | zh_TW |
| 顯示於系所單位： | 醫學工程學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-100-1.pdf 未授權公開取用 | 1.78 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。