| dc.contributor.advisor | 郭訓志 | zh_TW |
| dc.contributor.advisor | Kuo,Hsun-Chih | en_US |
| dc.contributor.author (作者) | 張仲凱 | zh_TW |
| dc.contributor.author (作者) | Chang,Chunf-Kai | en_US |
| dc.creator (作者) | 張仲凱 | zh_TW |
| dc.creator (作者) | Chang,Chunf-Kai | en_US |
| dc.date (日期) | 2005 | en_US |
| dc.date.accessioned | 2009-09-14 | - |
| dc.date.available | 2009-09-14 | - |
| dc.date.issued (上傳時間) | 2009-09-14 | - |
| dc.identifier (其他 識別碼) | G0093354012 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/30900 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 統計研究所 | zh_TW |
| dc.description (描述) | 93354012 | zh_TW |
| dc.description (描述) | 94 | zh_TW |
| dc.description.abstract (摘要) | 如何藉由DNA微陣列資料跟存活資料的資訊來找出基因表現定型一直是個重要的議題。這些研究的主要目標是從大量的基因中找出那些真正跟存活時間或其它重要的臨床結果有顯著關係的小部分。Threshold Gradient Directed Regularization (TGDR)是ㄧ種已經被應用在高維度迴歸問題中能同時處理變數選取以及模型配適的演算法。然而，TGDR採用一種梯度投影型態的演算法使得收斂速率緩慢。在本篇論文中，我們建議新的包含Newton-Raphson求解演算法類型的改良版TGDR方法。我們建議的方法有類似TGDR的特性但卻有比較快的收斂速率。文中並利用一筆附有設限存活時間的真實微陣列癌症資料來做示範。 本篇論文的第二部份是關於適用於區間設限存活資料的重複抽樣Peto-Peto檢定。這個重複抽樣Peto-Peto檢定能夠評估存活函數估計方法的檢定力，例如Turnbull的估計方法以及Kaplan-Meier的估計方法。這個檢定方法顯示出在區間設限資料時Kaplan-Meier的估計方法的檢定力要比Turnbull的估計方法的檢定力來得低。這個檢定方法將以模擬的區間設限資料以及一筆真實關於乳癌研究的區間設限資料來說明。 | zh_TW |
| dc.description.abstract (摘要) | Analyzing censored survival data with high-dimensional covariates arising from the microarray data has been an important issue. The main goal is to find genes that have pivotal influence with patient`s survival time or other important clinical outcomes. Threshold Gradient Directed Regularization (TGDR) method has been used for simultaneous variable selection and model building in high-dimensional regression problems. However, the TGDR method adopts a gradient-projection type of method and would have slow convergence rate. In this thesis, we proposed Modified TGDR algorithms which incorporate Newton-Raphson type of search algorithm. Our proposed approaches have the similar characteristics with TGDR but faster convergence rates. A real cancer microarray data with censored survival times is used for demonstration. The second part of this thesis is about a proposed resampling based Peto-Peto test for survival functions on interval censored data. The proposed resampling based Peto-Peto test can evaluate the power of survival function estimation methods, such as Turnbull’s Procedure and Kaplan-Meier estimate. The test shows that the power based on Kaplan-Meier estimate is lower than that based on Turnbull’s estimation on interval censored data. This proposed test is demonstrated on simulated data and a real interval censored data from a breast cancer study. | en_US |
| dc.description.tableofcontents | Chapter 1 Introduction ................................1 Chapter 2 cDNA Mircoarrays ............................3 2.1 The Central Dogma of Molecular Biology ............3 2.2 cDNA Microarrays Experiment .......................5 Chapter 3 Literature Review ...........................7 Chapter 4 Methodology ................................14 4.1 Cox Proportion Hazards Model (PH Model) ..........14 4.2 TGDR Algorithm ...................................15 4.3 Modified TGDR Algorithms .........................16 4.4 Tuning Parameter Selection .......................17 Chapter 5 Performance Evaluation via Prediction ......19 Chapter 6 Example Using Diffuse Large B-Cell Lymphoma (DLBCL) Data .........................................20 Chapter 7 A Resampling Based Peto-Peto Test for Survival Functions on Interval Censored Data ..................29 7.1 Kaplan-Meier (Product-Limit) Estimate ............29 7.2 Turnbull’s Procedure ............................29 7.3 Two-Step-Constrained Turnbull’s Procedure .......31 7.3.1 Standard Stochastic Ordering (for Multinomial Probability Vectors) .................................32 7.3.2 Uniform Stochastic Ordering ...................32 7.3.3 Likelihood Ratio Ordering ......................32 7.4 Generalized Log-Rank Test ........................33 7.5 Resampling Based Peto-Peto Test ..................33 7.6 Example ..........................................34 Chapter 8 Conclusion and Future Work .................39 References ...........................................41 | zh_TW |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0093354012 | en_US |
| dc.subject (關鍵詞) | 基因表現資料 | zh_TW |
| dc.subject (關鍵詞) | 設限存活資料 | zh_TW |
| dc.subject (關鍵詞) | Cox比例風險模型 | zh_TW |
| dc.subject (關鍵詞) | 重複抽樣Peto-Peto檢定 | zh_TW |
| dc.subject (關鍵詞) | Gene expression data | en_US |
| dc.subject (關鍵詞) | Censored survival data | en_US |
| dc.subject (關鍵詞) | Cox proportional hazards model | en_US |
| dc.subject (關鍵詞) | Rasmpling based Peto-Peto test | en_US |
| dc.subject (關鍵詞) | Threshold gradient directed regularization | en_US |
| dc.title (題名) | 應用存活分析在微陣列資料的基因表面定型之探討 | zh_TW |
| dc.title (題名) | Gene Expression Profiling with Survival Analysis on Microarray Data | en_US |
| dc.type (資料類型) | thesis | en |
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