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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 蕭朱杏(Chuhsing Kate Hsiao) | |
| dc.contributor.author | Chang-Xian She | en |
| dc.contributor.author | 佘昌憲 | zh_TW |
| dc.date.accessioned | 2021-06-15T13:02:59Z | - |
| dc.date.available | 2016-08-26 | |
| dc.date.copyright | 2016-08-26 | |
| dc.date.issued | 2016 | |
| dc.date.submitted | 2016-07-07 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/50863 | - |
| dc.description.abstract | 隨著生物技術的快速發展，越來越多的多基因平台資料(multi-platform genetic data)使得研究人員得以進行多平台的整合分析(integrative analysis)。然而，困難的是如何處理不同平台基因標記物(markers)資料間的關係、以及同一平台內標記物之間的相關性。另外，在關聯性研究中，以基因集合進行的遺傳分析已證實能夠比單一基因檢定(single-marker tests)方法有更高的檢定力(power)，因此，如何在整合分析中納入基因集合是目前關鍵的議題。本論文提出一個基於生物路徑的貝氏整合分析模型(Pathway-based Bayesian integrative analysis model, PaBIA model)來整合基因表現量與DNA甲基化兩種平台的資料，同時將生物路徑拓樸(pathway topology-based) 的概念納入模型中。透過後驗分佈的推論，可以在給定的生物路徑中偵測出有影響的基因，並且將他們的重要性進行排序。在模擬研究中，相較於傳統方法，這個PaBIA模型有較低的錯誤發現率(false discovery proportion)，及較高的真陰性率(true negative rate)，但是在(真陽性率+真陰性率)/2上則較傳統方法略差不到2%。最後，我們使用高程度乳腺管原位癌(high-grade ductal carcinoma in situ)的次世代定序資料以及卵巢癌的微陣列基因資料，透過分析KEGG的多個生物路徑來示範這個統計模型。實際資料分析中被PaBIA排為前幾名重要的基因都曾被文獻報導過與乳癌及卵巢癌的相關性，而且，某些基因已被做為治療乳癌或其他癌症的標靶基因。 | zh_TW |
| dc.description.abstract | The rapid advancement in biotechnology has made the genetic data from multiple platforms accessible for scientists to perform integrative analysis. Challenges arise, however, in dealing with the relationship between data from different sources, as well as the correlation between markers from the same platform. For statistical analysis, current set-based genetic analysis has been shown to exert more statistical power than single marker tests in association studies. Therefore, the incorporation of gene-sets into the integrative analysis has become a critical issue. In this thesis we propose a Pathway-based Bayesian integrative analysis (PaBIA) model to integrate RNA expression and DNA methylation data, simultaneously incorporating the concept of pathway topology to model the relationship between marker values. Based on the posterior inference, influential genes in given pathways can be identified and ranked. Simulation studies confirmed that the proposed model performed better than other traditional approaches, in terms of false discovery proportion and true negative rate. The (true positive rate +true negative rate)/2 of PaBIA is smaller than that of other methods by less than 2%. Finally, we illustrate this approach with a high-grade ductal carcinoma in situ study, and an ovarian cancer study, with KEGG pathways. The top ranking genes have been reported in previous literature to associate with breast cancer or ovarian cancer, and some have even been applied in target therapy. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T13:02:59Z (GMT). No. of bitstreams: 1 ntu-105-R03849033-1.pdf: 2040724 bytes, checksum: 3a502ac232815ee3b011e4b5a2e08679 (MD5) Previous issue date: 2016 | en |
| dc.description.tableofcontents | 第一章、 研究背景 1
第二章、 方法 4 第一節 符號與模式 4 第二節 計算與推論 6 第三節 資料處理及訊息模式設定 6 節點的設定 6 連結的設定 7 第三章、 模擬 8 第一節 設定 8 第二節 結果 10 第四章、 乳癌研究應用 11 第一節 背景與資料處理 11 正規化(Normalization) 11 離群值偵測(Outlier detection) 12 第三節 結果 12 Hyaluronan 13 Estrogen signaling pathway 14 MTOR pathway 14 第五章、 卵巢癌研究應用 17 第一節 資料背景 17 第二節 結果 17 Cell cycle、p53、mTOR、PI3K-Akt pathway 17 第六章、 討論 19 第七章、 參考文獻 22 | |
| dc.language.iso | zh-TW | |
| dc.subject | 基因表現 | zh_TW |
| dc.subject | 基因排序 | zh_TW |
| dc.subject | 基因表現 | zh_TW |
| dc.subject | DNA甲基化 | zh_TW |
| dc.subject | 貝氏模型 | zh_TW |
| dc.subject | 基因排序 | zh_TW |
| dc.subject | 整合分析 | zh_TW |
| dc.subject | 次世代定序 | zh_TW |
| dc.subject | 貝氏模型 | zh_TW |
| dc.subject | 生物路徑 | zh_TW |
| dc.subject | DNA甲基化 | zh_TW |
| dc.subject | 生物路徑 | zh_TW |
| dc.subject | 次世代定序 | zh_TW |
| dc.subject | 整合分析 | zh_TW |
| dc.subject | gene ranking | en |
| dc.subject | Bayesian model | en |
| dc.subject | DNA methylation | en |
| dc.subject | gene expression | en |
| dc.subject | integrative analysis | en |
| dc.subject | next generation sequencing | en |
| dc.subject | pathways | en |
| dc.subject | Bayesian model | en |
| dc.subject | DNA methylation | en |
| dc.subject | gene expression | en |
| dc.subject | gene ranking | en |
| dc.subject | integrative analysis | en |
| dc.subject | next generation sequencing | en |
| dc.subject | pathways | en |
| dc.title | 利用貝氏統計模式進行生物路徑之整合相關性研究分析 | zh_TW |
| dc.title | Pathway-based Bayesian integrative analysis for genetic association studies | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 104-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 盧子彬,楊欣洲,蔡政安 | |
| dc.subject.keyword | 貝氏模型,DNA甲基化,基因表現,基因排序,整合分析,次世代定序,生物路徑, | zh_TW |
| dc.subject.keyword | Bayesian model,DNA methylation,gene expression,gene ranking,integrative analysis,next generation sequencing,pathways, | en |
| dc.relation.page | 74 | |
| dc.identifier.doi | 10.6342/NTU201600753 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2016-07-07 | |
| dc.contributor.author-college | 公共衛生學院 | zh_TW |
| dc.contributor.author-dept | 流行病學與預防醫學研究所 | zh_TW |
| 顯示於系所單位： | 流行病學與預防醫學研究所 | |
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