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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳中明 | |
| dc.contributor.author | Tsung-Yeh Tsai | en |
| dc.contributor.author | 蔡宗曄 | zh_TW |
| dc.date.accessioned | 2021-06-12T18:02:28Z | - |
| dc.date.available | 2013-01-30 | |
| dc.date.copyright | 2008-01-30 | |
| dc.date.issued | 2008 | |
| dc.date.submitted | 2008-01-24 | |
| dc.identifier.citation | Balasubramaniyan, R., E. Hullermeier, et al. (2005). 'Clustering of gene expression data using a local shape-based similarity measure.' Bioinformatics 21(7): 1069-77.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/27362 | - |
| dc.description.abstract | 基因與生命現象間的關係在高通量分生技術高度發展的今日，得以有系統且全面性地進行探討與研究。對於鉅量而高雜訊的高通量訊息，生物資訊研究提供有效率的分析方法，促使分子生醫研究在後基因體時代之蓬勃發展。本研究以生物資訊之角度出發，針對基因體學中轉錄調控關係探討之議題，整合高通量訊息與生醫文獻，提出嶄新之模擬概念與關係預測演算法。
生命體以基因表現產物為各種行為與反應之作用者，而基因轉錄表現量的變化主要受到轉錄因子促進與抑制作用之調控，因此轉錄調控關係資訊可視為轉錄因子與生命現象之連結，能協助生醫研究在生化途徑、基因醫療等方面之發展。 染色體免疫沉澱晶片是目前少數能提供全面性轉錄調控關係訊息的方法，但其有高雜訊與顯著程度定義之問題，因此許多整合染色體免疫沉澱晶片與其他多元資料的關係預測生物資訊演算法被提出。然而其中多數研究對轉錄因子與受控基因兩者表現量間的關係，皆有悖於生物真實現象之假設，本研究即針對此問題，提出符合真實調控現象之關係預測演算法。 本研究先以小波除噪與二元化方法取得基因表現量顯著變化之時間序列，再提出轉錄因子活性機率樣式的概念，根據染色體免疫沉澱晶片資料篩選學習資料，推估轉錄因子在各時間點具調控活性之機率，並根據兩時間序列之相關性，以統計檢定方式判斷其調控型態與顯著性。 將之應用於酵母菌細胞週期資料，轉錄因子活性機率樣式符合已知作用階段訊息，且以十折交叉驗證顯示具有高度強健性；關係預測演算法則在十折交叉驗證和四筆高可信度驗證資料測試下，證明具有良好的預測能力，而對非細胞週期作用轉錄因子亦能維持其預測能力。最後並應用於酵母菌熱休克實驗資料，同樣具有強健性與高準確率，顯示本方法應用之廣度。 | zh_TW |
| dc.description.abstract | To depict real phenomenon of transcriptional regulation and improve the accuracy of identifying regulatory relation, a new serial pattern, called Transcription Factor Activity Probability Pattern (TFAPP) and prediction method are developed in this study. Transcriptional regulation is the keystone of biological systems; therefore, the regulatory relationship information is helpful to researches on biological pathway and genetic therapy.
We present TFAPP, the occurrence probability of regulation based on the co-expression phenomenon of targeted genes regulated by one TF, and identify regulatory relationship according to the correlation between TFAPP and binary gene expression pattern. Learning data of TFAPP are collected from ChIP-chip and microarray, and pre-processed by wavelet de-noise. TFAPP is estimated by binary factor analysis and random sampling process. In this research, the TFAPP has been proved meaningful for 37 yeast TFs in cell cycle condition, according to most well-known information, and its robustness has also been confirmed by 10-fold cross validation. High accuracy of prediction method based on TFAPP is validated by four high-confident targeted genes lists. In summary, the successful validation and application results of TFAPP with multiple conditions data reveal the extensity and potentiality of this study. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-12T18:02:28Z (GMT). No. of bitstreams: 1 ntu-97-R94548061-1.pdf: 1967422 bytes, checksum: 171be3d16a8bbe2d4170f4255c0fc405 (MD5) Previous issue date: 2008 | en |
| dc.description.tableofcontents | 口試委員會審定書 I
謝 誌 II 摘 要 III Abstract IV 目 錄 V 圖目錄 VIII 表目錄 X 第一章 序論 1 1.1 研究背景 1 1.2 研究動機 1 1.3 研究目的 3 1.4 論文架構 4 第二章 文獻探討 5 2.1 引言 5 2.2 基因表現調控機制 6 2.3 基因調控網路之模擬 8 2.3.1 常微分方程模型 8 2.3.2 布林網路 9 2.3.3 連續型網路 10 2.4 轉錄因子活性之模擬 12 2.4.1 模擬轉錄因子活性之目的 12 2.4.2 因素分析形式TFA演算法 15 2.4.3 酵素動力方程式形式TFA演算法 16 2.5 目標基因預測之文獻探討 17 第三章 研究材料與方法 19 3.1 研究流程 19 3.2 研究材料 20 3.2.1 染色體免疫沉澱晶片資訊 20 3.2.2 基因表現量資料 22 3.3 資料前處理 23 3.3.1 基因表現量二元化 23 3.3.2 小波除噪與平滑化 24 3.4 TFAPP演算法 27 3.4.1 TFAPP定義 27 3.4.2 學習資料 28 3.4.3 二元資料因素分析 29 3.4.4 演算流程 30 3.5 調控關係之判斷 33 3.5.1 Spearman 等級相關檢定 33 3.5.2 錯誤發現率控制 34 3.5.3 演算流程 35 第四章 研究結果與討論 37 4.1 TFAPP推估結果討論 37 4.1.1 細胞週期作用階段驗證 38 4.1.2 TFAPP強健性測試 40 4.2 預測能力驗證 43 4.2.1 十折交叉驗證 45 4.2.2 序列高可信度資料測試 47 4.2.3 生物實驗資料測試 50 4.2.4 TRANSFAC資料測試 51 4.2.5 生醫文獻資料測試 53 4.3 熱休克資料測試 55 4.3.1 背景與材料 55 4.3.2 十折交叉驗證 56 4.3.3 序列高可信度資料測試 59 4.3.4 TRANSFAC資料測試 60 4.4 結果比較 61 4.4.1 TRIA演算法 61 4.4.2 真陽率比較結果 62 4.4.3 ROC曲線比較結果 66 第五章 結論與展望 68 5.1 結論 68 5.2 未來研究方向 70 5.2.1 研究成果之應用 70 5.2.2 研究發展與改進方向 71 參考文獻 72 附錄A 細胞週期已知轉錄因子十折交叉驗證TFAPP推估結果 80 附錄B 生物實驗資料測試詳細結果 84 附錄C 生醫文獻測試資料參考文獻 86 附錄D 熱休克資料十折交叉驗證TFAPP推估結果 88 附錄E TRIA實作再現結果 96 附錄F TRIA驗證結果 97 作者履歷 100 | |
| dc.language.iso | zh-TW | |
| dc.subject | 因素分析 | zh_TW |
| dc.subject | 小波去噪 | zh_TW |
| dc.subject | 轉錄因子 | zh_TW |
| dc.subject | 轉錄因子活性機率樣式 | zh_TW |
| dc.subject | 調控關係 | zh_TW |
| dc.subject | Transcriptional regulation | en |
| dc.subject | Wavelet de-noise | en |
| dc.subject | Factor analysis | en |
| dc.subject | Transcription Factor Activity Probability Pattern | en |
| dc.subject | Transcription factor | en |
| dc.title | 利用轉錄因子活性機率樣式預測轉錄調控關係 | zh_TW |
| dc.title | A New Approach to Identifying Transcriptional Regulatory Relationship Based on Transcription Factor Activity Probability Pattern | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 96-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 蔡懷寬,陳倩瑜,黃乾綱 | |
| dc.subject.keyword | 轉錄因子,調控關係,轉錄因子活性機率樣式,因素分析,小波去噪, | zh_TW |
| dc.subject.keyword | Transcription factor,Transcriptional regulation,Transcription Factor Activity Probability Pattern,Factor analysis,Wavelet de-noise, | en |
| dc.relation.page | 79 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2008-01-24 | |
| 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 | |
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