臺灣博碩士論文加值系統

近年來使用基因微陣列時序資料，來重建基因調控網路，已經是一個非常普遍的方式，然而這些基因的數量通常是非常龐大的，因此我們想要在重建之前，先對這些基因做一個適當的分群，也就是彼此間有互動的基因會被分在同一個群組裡，在過去有許多分群的演算法，都已經被應用在基因的分群上。
在這邊我們使用的方式是結合多個資料來源，一方面避免受單一資料來源的影響太大，當只有一個資料來源時，資料的品質會對分群和重建有很大的影響。另一方面希望來對分群的績效會有一定的改善，進而達到提高後續重建，基因調控網路的正確性，在我們的研究裡，結合了兩種不同型態的資料來源，一個是基因微陣列時序資料，另一個是基因本體論，我們將基因本體論的資訊量化，再和時序資料做結合，結合的方式是使用座標放大縮小的方法，來調整時序資料和基因本體論間的權重，每個分群都會有二十一個權重，最後每個權重都分別使用分割式的分群演算來做分群，重建的部份以布林網路為主要工具。
經過我們的方法實驗後，我們可以看到，當我們有同時使用，微陣列時序資料，及基因本體論做分群時，其成效都會比單獨使用其中一個來分群還要好，而在後續的重建裡，當分群結果較好時，也可以得到較好的重建結果，因此我提出的方法，在對於分群基因上，是有效且可行的。

In recent years, using microarray time series data to reconstruct gene regulatory network, has become a very popular way. However, the number of these genes are usually very large. We want to rebuild before these genes do a proper clustering, which is the each other interaction between the genes will be divided in the same group.
The way we use here is to combine multiple data sources. On the one hand avoid being affected by the impact of a single data source. When there is only one data source, data quality will have a great impact. On the other hand, we hope to have some clustering performance improvement, and improve the subsequent reconstruction of the accuracy of gene regulatory network. In our study, we combine two different types of data sources. One of source is microarray time series data, the other is the Gene Ontology. We quantify Gene Ontology, and combine with time series data. Finally, we use the partition clustering algorithm to cluster, and use Boolnet to reconstruct gene regulatory network.
After our experiment, we can obtain more great performance when we use microarray time series data and Gene Ontology simultaneously. In the following reconstruction, when the clustering result is better, we can get a better reconstruction of gene regulatory network. Therefore, our method for clustering of gene is effective and feasible.

論文審定書 i
中文摘要 ii
英文摘要 iii
1.序論 1
1.1 研究背景 1
1.2 研究動機與目標 2
2. 文獻探討 4
2.1 基因分群 4
2.1.1 k-means 4
2.1.2 fuzzy k-means 5
2.2 Gene Ontology 6
2.2.1 GO term相似度測量 7
2.2.2 基因相似度計算 11
2.3 模擬基因調控網路 12
3. 研究方法 14
3.1 基因分群 14
3.2 引進GO協助基因分群 14
3.3 評估分群 16
3.4 使用布林網路推論基因調控網路 17
3.5 網路評估 18
3.6 實驗設計 19
3.6.1 分群前資料處理 19
3.6.2 分群及權重調整 19
3.6.3 重建基因調控網路 19
3.7 實驗流程圖 20
4. 實驗結果與討論 22
4.1 實驗資料 22
4.1 分群實驗結果 23
4.1.1 模糊分群PPI評估 23
4.1.2 模糊分群的群聚系數 26
4.1.3 fuzzy-cmeans與k-means分群的PPI 29
4.1.4 模糊程度對分群的影響 31
4.2.1 使用較高PPI值與較低PPI值重建結果 33
4.2.2 隨機分群後重建結果 37
4.2.3 k-means之重建結果 44
4.3 討論與分析 46
4.3.1 分群 46
4.3.2 重建基因調控網路 47
5. 結論與未來研究 54
6. 參考文獻 56

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