與核糖核酸(RNA)結合的蛋白質在核糖核酸中序列的辨識上占有很重要的位置，因為這些資訊是去氧核糖核酸(DNA)的作用來源。為了符合各種功能的需求，與核糖核酸結合的蛋白質是由許多重覆的結合區段組成，而這些區段各有其結構上的位置以提供不同的功能。應用機器學習方法於預測核糖核酸與蛋白質結合位置，可以協助分子生物研究人員快速過濾可能與RNA作用位置及機制。
ProteRNA為本論文所提出的預測方法，融合了支援向量機(SVM)與WildSpan蛋白質序列探勘兩種工具的結果，其中SVM利用PSSM及蛋白質二級結構資訊預測，而WildSpan則利用序列保留特質做預測。單純使用SVM方法的預測效能其F-score為0.5127，合併WildSpan 的預測結果F-score提升至 0.5362，相較目前其他預測方法表現較好。進行獨立測試時，ProteRNA可達到整體精確度89.55 %、Matthew`s 相關係數(MCC) 0.2686、及F-score 0.3185，超越其他現有的線上RNA與蛋白質結合位置預測服務。

RNA-binding proteins (RBPs) are vital for recognition sequences of ribonucleic acids, which is the genetic material that is derived from the DNA. For satisfying diverse functional requirements, RNA binding proteins are composed of multiple repeated blocks of RNA-binding domains presented in various structural arrangements to provide versatile functions. The ability to predict computationally RNA-binding residues in a RNA-binding protein can help biologists to have clues on site-directed mutagenesis in wet-lab experiments. “ProteRNA” is the proposed prediction framework in this thesis, combining Support Vector Machine (SVM) and WildSpan for identifying RNA-interacting residues in a RNA-binding protein. SVM utilizes PSSM and protein secondary structure information to predict, while WildSpan bases on conserved domain information. The performances of SVM predictor are F-score of 0.5127; however, the performances of the WildSpan hybrid predictor achieve F-score of 0.5362. In the independent testing dataset, ProteRNA has been able to deliver overall accuracy of 89.55 %, MCC of 0.2686, and F-score of 0.3185. ProteRNA surpasses the other web servers no matter in terms of accuracy, MCC, or F-score.

Table of Contents
致謝 I
摘要 II
Abstract III
Table of Contents IV
List of Figures VI
List of Tables VII
Chapter 1 Introduction 1
1-1 Background 1
1-2 Motivation 4
1-3 Summary of Paper Organization 5
Chapter 2 Literature Review 6
2-1 Central Dogma 6
2-2 The Attributes of Amino Acid 9
2-3 Position-Specific Scoring Matrix 11
2-4 Secondary Structure Information 12
2-5 Classifier - Support Vector Machines 12
2-6 WildSpan 18
2-7 Related Works 18
Chapter 3 Method 23
3-1 Problem Definition 23
3-2 Data Set 23
3-3 Performance Measure 25
3-4 Feature Selection 26
3-5 Normalization 28
3-6 Single Predictor Model 30
3-7 Hybrid Model 33
3-8 System Architecture 34
Chapter 4 Results and Discussion 38
4-1 Distinct Normalization Results 38
4-2 Performance of Single Predictor 39
4-3 Performance of Hybrid Model 43
4-4 Comparison with Other Approaches 47
4-5 Independent Test and Comparison with Other Approaches 49
4-6 Independent Test Case Discussion 51
Chapter 5 Conclusion and Further Directions 57
5-1 Conclusion 57
5-2 Further Directions 58
References 60

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