臺灣博碩士論文加值系統

蛋白質交互作用是生物體產生各種功能的基礎。透過蛋白質交互作用的研究，可以理解細胞運作的基本原理，進而開發、設計藥物，並針對疾病進行治療。蛋白異構體 (isoform) 是指同一基因經由選擇性剪接 (alternative splicing) 所產生的不同產物，若能進一步了解蛋白異構體的交互作用，不論在基礎或臨床的研究上都非常重要。而近幾年高通量mRNA定序 (RNA-Seq) 提供了蛋白異構體表現量之數據，有利於我們更深入地了解蛋白異構體之交互作用。
本篇研究主要是以貝葉斯機率模型 (Bayesian probability model) 為基礎，有效地整合不同型態的資料，分別是蛋白質異構體表現量 (isoform expression)、蛋白質功能域交互作用 (domain-domain interactions dataset)、基因註解 (gene ontology, GO) 以及直系人類同源蛋白 (orthologous human proteins) 資料，作為我們預測蛋白質異構體交互作用的推論證據。最後，我們將預測結果與其他預測方法進行比較，並且試圖建構出更完整的人類蛋白異構體交互作用網路。

Protein interactions are the basis of organism functions. Through protein interaction studies, we can understand the basic principles of cell activity. Then develop and design drugs for the disease treatment. Protein isoforms is generated by alternative splicing from the same gene. If we have deeply understanding of the interactions of protein isoforms, either in basic or clinical research is very important. In recent years, high-throughput mRNA sequencing (RNA-Seq) provides isoform-level expression data, which helps us further understanding the interactions of protein isoforms.
This study is based on Bayesian probabilistic model to effectively integrate different types of information: the mRNA expression, domain-domain interactions, gene annotation and Orthologous human protein datasets, as the inference evidence of isoform-isoform interaction prediction. Finally, we compared the predicted results with other prediction methods, and attempted to construct complete human isoform-isoform interaction networks.

論文摘要 i
Abstract ii
目錄 iii
表目錄 v
圖目錄 vi
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的與貢獻 2
1.3 論文架構 2
第二章 相關研究 4
2.1 蛋白異構體交互作用 4
2.2 研究蛋白質交互作用之方法 6
2.2.1 蛋白質功能域方法 6
2.2.2 基因同源性方法 7
2.2.3 機器學習方法 8
2.2.4 機率模型方法 8
第三章 研究方法 10
3.1 資料收集、前置作業與處理 10
3.1.1 Golden standard interactions 11
3.1.2 Training and test sets 12
3.1.3 RNA-seq datasets 13
3.1.4 Domain-domain interactions database 13
3.1.5 UniProt GO annotation 14
3.1.6 Orthologous human interaction 15
3.1.7 Isoform and KnownGene mapping 16
3.2 貝葉斯機率方法 19
3.2.1 Bayesian network approach 19
3.2.2 Prior odds of isoform-isoform interactions 21
3.3 貝葉斯機率模型流程與架構 22
3.3.1 Gene co-expression 23
3.3.2 Domain pair enrichment 24
3.3.3 Smallest shared biological process 26
3.3.4 Ortholog Interactomes 26
3.3.5 Likelihood ratio calculation 27
3.4 模型效能評估 28
第四章 研究結果與討論 30
4.1 貝葉斯機率模型預測結果 30
4.1.1 比較使用LR與RNA-seq correlation 30
4.1.2 比較使用LR與SSBP 32
4.1.3 比較使用LR與Ortholog score 33
4.1.4 使用蛋白質功能域交互作用推論 isoform-isoform interaction 之結果 34
4.1.5 使用整合性證據推論 isoform-isoform interaction 之結果 35
4.2 分析方法比較 37
4.2.1 比較迴歸模型與貝葉斯機率模型預測結果 37
4.2.2 比較SVM模型與貝葉斯機率模型預測結果 41
第五章 結論與未來展望 43
5.1 結論 43
5.2 未來展望 44
參考文獻 45
附錄一 50
附錄二 56

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