運用時間序列微陣列資料來預測調控基因

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姓名

謝佩君(Pei-Chun Hsieh) 查詢紙本館藏

畢業系所

系統生物與生物資訊研究所

論文名稱

運用時間序列微陣列資料來預測調控基因
(Predicting Regulating Genes using Time-course Microarray Data)

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摘要(中)

我們用兩組酵母菌和兩組細菌的微陣列數據來驗證以及比較三個用來研究基因調控的動力模型―一階、二階和平均。對於每一個受關注的被調控基因，我們以最高事後機率找出最有可能的調控基因，接著利用EchoBASE、GeneDB以及Saccharomyces Genome Database等資料庫來查對那些最高事後機率的基因，並認定其中哪些被註釋為轉錄因子。我們將最大化機率的基因分成兩組-編碼轉錄因子與編碼非轉錄因子，再以它們的ROC線下面積（AUC）來量化模型的表現。我們發現二階模型在驗證中的整體表現最佳，其中一個AUC高達100%。因此在本研究中，我們認為二階微分模型預測轉錄因子的能力會優於其他兩種動力模型。

摘要(英)

On two yeast datasets and two bacteria datasets, we validate and compare three kinetic models – first-order, second-order and averaged – for studying causal relationships between genes. For each regulated gene of interest, we identify the gene with the highest posterior probability of being its dominant regulating gene. We then check the annotation of those genes with the highest posterior probability（probability-maximizing genes）in EchoBASE, GeneDB, and in the Saccharomyces Genome Database and note which among them are putative transcription factors. To quantify performance, we estimate the area under the receiver operating characteristic curve (AUC) between the probability-maximizing genes that encode putative transcription factors and those that do not. We find the second-order model performs well in validation. One of its AUC estimates is 100%, reflecting the case in which the only putative transcription factor has a higher posterior probability of being the dominant regulator of a gene of interest than any of the other 42 genes. Based on this study we suggest that the second-order model is better than others kinetic models on predicting transcription factors.

關鍵字(中)

★ 時間序列微陣列
★ 調控基因

關鍵字(英)

★ regulating gene
★ time-course microarray

論文目次

第一章、簡介 1
1-1 微陣列晶片 1
1-1-1 背景 1
1-1-2 實驗流程 1
1-1-3 微陣列的分析 4
1-2 基因調控關聯性網路的重建 6
1-2-1 貝氏網路 6
1-2-2 線性的轉錄模型 7
第二章、資料內容與資料處理 9
2-1 酵母菌與大腸桿菌的時間序列微陣列資料 9
2-2 本研究有興趣的標靶基因 11
2-2-1 酵母菌的標靶基因 11
2-2-2 大腸桿菌標靶基因之選取 13
2-3 遺漏值的成因 14
2-4 遺漏值的處理方法 15
第三章、基因網路模型 18
3-1 轉錄的調控網路模型 18
3-2 一階微分方程式模型 21
3-3 二階微分方程式模型 24
3-4 平均模型 26
第四章、分析結果 27
4-1 標靶基因之預測結果 27
4-1-1 酵母菌標靶基因之預測結果 27
4-1-2 大腸桿菌標靶基因之預測結果 30
4-2 各模型的分析結果 32
4-3 三個模型的分析優劣之比較 36
第五章、討論 39
參考文獻 43
附錄 46
附錄一本研究挑選出的酵母菌被調控基因（S. cerevisiae regulated genes） 46
附錄二本研究挑選出的大腸桿菌被調控基因（E. coli regulated genes） 47

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指導教授

李弘謙、凌慶東
(Hoong-Chien Lee、Qing-Dong Ling)

審核日期

2009-7-10

推文