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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 黃乾綱(Chien-Kang Huang) | |
dc.contributor.author | Shu-Yu Kang | en |
dc.contributor.author | 康書語 | zh_TW |
dc.date.accessioned | 2021-06-16T16:06:04Z | - |
dc.date.available | 2018-07-03 | |
dc.date.copyright | 2013-07-03 | |
dc.date.issued | 2013 | |
dc.date.submitted | 2013-06-19 | |
dc.identifier.citation | 1. Schickel, R., et al., MicroRNAs: key players in the immune system, differentiation, tumorigenesis and cell death. Oncogene, 2008. 27(45): p. 5959-5974.
2. Bartel, D.P., MicroRNAs: Genomics, Biogenesis, Mechanism, and Function. Cell, 2004. 116(2): p. 281-297. 3. Jha, A. and R. Shankar, Employing machine learning for reliable miRNA target identification in plants. BMC Genomics, 2011. 12(1): p. 636. 4. Bandyopadhyay, S. and R. Mitra, TargetMiner: microRNA target prediction with systematic identification of tissue-specific negative examples. Bioinformatics, 2009. 25(20): p. 2625-2631. 5. Dai, X. and P. Zhao, psRNATarget; a plant small RNA target analysis server. Nucleic Acids Res, 2011: p. 1 - 5. 6. Xie, F. and B. Zhang, Target-align: a tool for plant microRNA target identification. Bioinformatics, 2010. 23: p. 3002 - 3003. 7. Ambros, V., microRNAs: Tiny Regulators with Great Potential. Cell, 2001. 107(7): p. 823-826. 8. Wightman, B., I. Ha, and G. Ruvkun, Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans. Cell, 1993. 75(5): p. 855-862. 9. PILLAI, R.S., MicroRNA function: Multiple mechanisms for a tiny RNA? RNA, 2005. 11(12): p. 1753-1761. 10. Saito, T. and P. Satrom, MicroRNAs – targeting and target prediction. New Biotechnology, 2010. 27(3): p. 243-249. 11. Lekprasert, P., M. Mayhew, and U. Ohler, Assessing the Utility of Thermodynamic Features for microRNA Target Prediction under Relaxed Seed and No Conservation Requirements. PLoS ONE, 2011. 6(6): p. e20622. 12. Kertesz, M., et al., The role of site accessibility in microRNA target recognition. Nat Genet, 2007. 39(10): p. 1278-1284. 13. Zhao, Y., E. Samal, and D. Srivastava, Serum response factor regulates a muscle-specific microRNA that targets Hand2 during cardiogenesis. Nature, 2005. 436(7048): p. 214-220. 14. Karolchik, D., et al., The UCSC Genome Browser Database. Nucleic Acids Research, 2003. 31(1): p. 51-54. 15. Wu, H.-J., et al., PsRobot: a web-based plant small RNA meta-analysis toolbox. Nucleic Acids Research, 2012. 40(W1): p. W22-W28. 16. Sethupathy, P., M. Megraw, and A.G. Hatzigeorgiou, A guide through present computational approaches for the identification of mammalian microRNA targets. Nat Meth, 2006. 3(11): p. 881-886. 17. Miranda, K.C., et al., A Pattern-Based Method for the Identification of MicroRNA Binding Sites and Their Corresponding Heteroduplexes. Cell, 2006. 126(6): p. 1203-1217. 18. Orom, U.A., F.C. Nielsen, and A.H. Lund, MicroRNA-10a Binds the 52UTR of Ribosomal Protein mRNAs and Enhances Their Translation. Molecular cell, 2008. 30(4): p. 460-471. 19. Tay, Y., et al., MicroRNAs to Nanog, Oct4 and Sox2 coding regions modulate embryonic stem cell differentiation. Nature, 2008. 455(7216): p. 1124-1128. 20. Grimson, A., et al., MicroRNA Targeting Specificity in Mammals: Determinants beyond Seed Pairing. Molecular cell, 2007. 27(1): p. 91-105. 21. Axtell, M.J., et al., A Two-Hit Trigger for siRNA Biogenesis in Plants. Cell, 2006. 127(3): p. 565-577. 22. Doench, J.G. and P.A. Sharp, Specificity of microRNA target selection in translational repression. Genes & Development, 2004. 18(5): p. 504-511. 23. Dai, X., Z. Zhuang, and P.X. Zhao, Computational analysis of miRNA targets in plants: current status and challenges. Briefings in Bioinformatics, 2011. 12(2): p. 115-121. 24. Vapnik, V.N., The nature of statistical learning theory1995: Springer-Verlag New York, Inc. 188. 25. Kruger, J. and M. Rehmsmeier, RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Res, 2006. 34: p. W451 - 4. 26. Collobert, R. and S. Bengio, SVMTorch: support vector machines for large-scale regression problems. J. Mach. Learn. Res., 2001. 1: p. 143-160. 27. Dai, X. and P.X. Zhao, psRNATarget: a plant small RNA target analysis server. Nucleic Acids Research, 2011. 39(suppl 2): p. W155-W159. 28. Muckstein, U., et al., Thermodynamics of RNA–RNA binding. Bioinformatics, 2006. 22(10): p. 1177-1182. 29. Xie, F. and B. Zhang, Target-align: a tool for plant microRNA target identification. Bioinformatics, 2010. 26(23): p. 3002-3003. 30. Smith, T.F. and M.S. Waterman, Identification of common molecular subsequences. Journal of Molecular Biology, 1981. 147(1): p. 195-197. 31. Enright, A., et al., MicroRNA targets in Drosophila. Genome Biology, 2003. 5(1): p. R1. 32. Kung, D.-M., A Study of RNA Features for MicroRNA Target Prediction, in M.S. thesis, National Taiwan University2011. 33. Chang, C.-C. and C.-J. Lin, LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol., 2011. 2(3): p. 1-27. 34. Beauclair, L., A. Yu, and N. Bouche, microRNA-directed cleavage and translational repression of the copper chaperone for superoxide dismutase mRNA in Arabidopsis. The Plant journal : for cell and molecular biology, 2010. 62(3): p. 454-462. 35. Gustafson, A.M., et al., ASRP: the Arabidopsis Small RNA Project Database. Nucleic Acids Research, 2005. 33(suppl 1): p. D637-D640. 36. Griffiths-Jones, S., et al., miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Research, 2006. 34(suppl 1): p. D140-D144. 37. Garcia-Hernandez, M., et al., TAIR: a resource for integrated Arabidopsis data. Functional & Integrative Genomics, 2002. 2(6): p. 239-253. 38. Betel, D., et al., Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biology, 2010. 11(8): p. R90. 39. Hofacker, I.L., Vienna RNA secondary structure server. Nucleic Acids Research, 2003. 31(13): p. 3429-3431. 40. Tafer, H., et al., The impact of target site accessibility on the design of effective siRNAs. Nat Biotech, 2008. 26(5): p. 578-583. 41. Bernhart, S.H., I.L. Hofacker, and P.F. Stadler, Local RNA base pairing probabilities in large sequences. Bioinformatics, 2006. 22(5): p. 614-615. 42. Bompfunewerer, A., et al., Variations on RNA folding and alignment: lessons from Benasque. Journal of Mathematical Biology, 2008. 56(1-2): p. 129-144. 43. Ding, J., S. Zhou, and J. Guan, miRFam: an effective automatic miRNA classification method based on n-grams and a multiclass SVM. BMC Bioinformatics, 2011. 12(1): p. 216. 44. Sturm, M., et al., TargetSpy: a supervised machine learning approach for microRNA target prediction. BMC Bioinformatics, 2010. 11(1): p. 292. 45. Rijsbergen, C.J.V., Information Retrieval1979: Butterworth-Heinemann. 208. 46. Kira, K. and L.A. Rendell, A practical approach to feature selection, in Proceedings of the ninth international workshop on Machine learning1992, Morgan Kaufmann Publishers Inc.: Aberdeen, Scotland, United Kingdom. p. 249-256. 47. Kononenko, I., Estimating attributes: Analysis and extensions of RELIEF, in Machine Learning: ECML-94, F. Bergadano and L. Raedt, Editors. 1994, Springer Berlin Heidelberg. p. 171-182. 48. Hall, M., et al., The WEKA data mining software: an update. SIGKDD Explor. Newsl., 2009. 11(1): p. 10-18. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/62631 | - |
dc.description.abstract | 植物的微型核醣核酸(microRNA)屬於非編碼核醣核酸(non-coding RNA),平均約為19-22核苷酸長,它能抑制目標基因轉譯成蛋白質和對目標基因進行裁切,進而影響許多重要的生物反應。經由生物實驗尋找微型核醣核酸目標基因需耗費大量的時間及成本,因此開發能有效預測目標基因的演算法便成了重要的議題。現有的預測工具,大多運用以下六大類廣為生物界認可的特徵作為預測準則:互補性(Complementarity)、結合體熱動力穩定性(Thermodynamic Stability for Duplex)、區段可鍵結性(Site Accessibility)、演化保留性(Evolutionary Conservation)、序列位置特性(Site Location)與多重鍵結特性(Multiplicity of Binding Sites)。
在本研究中,以前述六大類特徵為基礎,盡可能對各類特徵別進行全方位的考慮,配合支持向量機(Support Vector Machine, SVM)的使用,對植物的微型核醣核酸目標基因進行預測,並透過特徵挑選來評估各類特徵的重要性。經由在阿拉伯芥(Arabidopsis thaliana)上所做的獨立實驗驗證,本研究的演算法相較於其他現有預測方法,有最佳的預測表現:準確度(Precision)100%、正確度(Accuracy)97.8%、敏感度(Sensitivity)97.1%、特異性(Specificity)100%。由RELIEF-F方法的特徵挑選(Feature Selection)結果顯示,微型核醣核酸與核醣核酸鍵結的最小自由能(Minimum Free Energy, MFE)為最重要的特徵。另外,兩兩核苷酸組成(Bigram)與本研究中新加入的三三核苷酸組成(Trigram)亦扮演著相當重要的角色。 | zh_TW |
dc.description.abstract | Plant microRNAs (miRNAs) are small non-coding RNAs consisting of 19-22 nucleotides. MiRNAs play an important role in gene regulation and affect many follow-up biological interactions either by suppressing the translation of target genes to proteins or by the cleavage of the target genes.
Due to the costly and time-consuming biochemical experiment process to verify a target gene, computational methods are developed to screen out candidates that are not likely to be the targets. Most current prediction tools develop their algorithm based on six categories of features that are commonly recognized and reported to be important in miRNA-mRNA interactions. These six categories are complementarity, thermodynamic stability for duplex, site accessibility, evolutionary conservation, site location and multiplicity of binding sites. In this research, all the six categories of features along with proposed features are considered. This research uses machine learning based algorithms “Support Vector Machine (SVM)” as classifier to predict plant miRNA binding targets, followed by a feature selection phase using RELIEF-F method. In an independent test on Arabidopsis thaliana, the proposed tool can achieve the prediction result with the precision of 100%, accuracy of 97.8%, sensitivity of 97.1%, and specificity of 100%. Moreover, according to the result of RELIEF-F scores in feature selection, minimum free energy (MFE) of miRNA-mRNA duplex appears to be the most important feature, followed by the bigram and trigram features. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T16:06:04Z (GMT). No. of bitstreams: 1 ntu-102-R00525044-1.pdf: 1239342 bytes, checksum: 808ed7b7e0701eaf416cfa427749dce3 (MD5) Previous issue date: 2013 | en |
dc.description.tableofcontents | 致謝 ii
摘要 iii ABSTRACT iv CONTENTS v LIST OF FIGURES vii LIST OF TABLES viii Chapter 1 Introduction 1 1.1 Background 1 1.2 Motivation 2 1.3 Contribution 3 1.4 Summary of Thesis Organization 3 Chapter 2 Related Work 5 2.1 MicroRNA (miRNA) 5 2.2 Attributes of miRNA-mRNA Interactions in Plants 6 2.3 Current Computational Methods of MiRNA Target Prediction 10 2.3.1 P-TAREF 12 2.3.2 PsRNATarget 13 2.3.3 Target-align 13 2.3.4 TargetMiner 14 2.4 Local Alignment Algorithm: Smith-Waterman 15 2.5 Support Vector Machine (SVM) 18 Chapter 3 Materials and Method 23 3.1 Idea and Observation 23 3.2 Materials 24 3.2.1 Data Collection 24 3.2.2 Data Collection 25 3.3 System Architecture 26 3.4 Target Recognition 27 3.5 Filter Process 29 3.6 Comprehensive Feature Extraction 31 3.6.1 Seed and Seed-out Complementarity Features 32 3.6.2 Contextual Features 34 3.6.3 Thermodynamic Feature 36 3.6.4 Accessibility Features 37 3.6.5 Position Specific Features 37 3.6.6 Trigram Features 38 3.6.7 Non Watson-Crick Pairing Features 39 3.6.8 Compactness Feature 40 3.6.9 Central Region Feature 41 3.6.10 Summary 41 Chapter 4 Results 44 4.1 Evaluation Indices 44 4.2 Performance Evaluation 46 4.3 Comparison with Other Approaches 48 4.4 Feature Selection 50 4.5 Evaluation on Degradome Data 52 Chapter 5 Discussion and Conclusion 58 Chapter 6 Reference 60 Supplement 63 | |
dc.language.iso | en | |
dc.title | 應用支持向量機於植物核醣核酸對微型核醣核酸目標基因預測 | zh_TW |
dc.title | Plant MicroRNA-mRNA Target Prediction Using Support Vector Machine | en |
dc.type | Thesis | |
dc.date.schoolyear | 101-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 張瑞益,張天豪,林詩舜 | |
dc.subject.keyword | 植物微型核醣核酸,預測目標基因方法,特徵擷取,機器學習, | zh_TW |
dc.subject.keyword | plant miRNA,target gene prediction method,feature extraction,machine learning, | en |
dc.relation.page | 71 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2013-06-19 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
顯示於系所單位: | 工程科學及海洋工程學系 |
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