必要性蛋白質對細胞生命影響非常深，但我們很難去偵測必要性蛋白質。蛋白質交互作用為其中一種檢測蛋白質是否為必要性蛋白質的方法。我們注意到很多研究方法從蛋白質交互作用擷取拓樸的特徵去預測必要性蛋白質。然而，蛋白質的功能也是一條線索去決定他的必要性。在本篇論文中，我們利用影響蛋白質功能的序列特徵、拓樸和蛋白質特徵去建立支持向量機的模型來預測必要性蛋白質。在我們的實驗中，我們從DIP資料庫中下載Scere20070107檔案，其中包含了4873條蛋白質和17166個交互作用。在此檔案中必要性蛋白質和非必要性蛋白質的比例相當不平衡為1:4。在不平衡的資料中，我們的模型得到最好的F-measure、MCC、AIC和BIC分別為0.5197、0.4371、0.2428和0.2543。我們另外建立了比例為1:1的平衡資料。在平衡資料中，我們的模型得到最好的F-measure、MCC、AIC和BIC分別為0.7742、0.5484、0.3603和0.3828。我們的研究結果均優於以前的研究方法與結果 。

Essential proteins affect the cellular life deeply, but it is hard to identify them. Protein-protein interaction is one of the ways to disclose whether a protein is essential or not. We notice that many researchers use the feature set composed of topology properties from protein-protein interaction to predict the essential proteins. However, the functionality of a protein is also a clue to determine its essentiality. In this thesis, to build SVM models for predicting the essential proteins, our feature set contains the sequence properties which can influence the protein function, topology properties and protein properties. In our experiments, we download Scere20070107, which contains 4873 proteins and 17166 interactions, from DIP database. The ratio of essential proteins to nonessential proteins is nearly 1:4, so it is imbalanced. In the imbalanced dataset, the best values of F-measure, MCC, AIC and BIC of our models are 0.5197, 0.4671, 0.2428 and 0.2543, respectively. We build another balanced dataset with ratio 1:1. For balanced dataset, the best values of F-measure, MCC, AIC and BIC of our models are 0.7742, 0.5484, 0.3603 and 0.3828, respectively. Our results are superior to all previous results with various measurements.

ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0
Chapter 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Chapter 2. Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1 Support Vector Machine . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Database of Protein and PPI . . . . . . . . . . . . . . . . . . . . . . . 8
2.3 Position Specific Scoring Matrix . . . . . . . . . . . . . . . . . . . . . 8
2.4 Topological Properties . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.4.1 Degree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.4.2 Bottleneck (BN) . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.4.3 Edge Percolated Component (EPC) . . . . . . . . . . . . . . . 12
2.4.4 Maximum Neighborhood Component (MNC) . . . . . . . . . . 12
2.4.5 Density of Maximum Neighborhood Component (DMNC) . . 12
2.4.6 Neighbors’ Intra-degree (NID ) . . . . . . . . . . . . . . . . . 14
2.4.7 Clustering Coefficient (CCo) . . . . . . . . . . . . . . . . . . . 14
2.4.8 Betweenness Centrality (BC) . . . . . . . . . . . . . . . . . . 15
2.4.9 Closeness Centrality (CC) . . . . . . . . . . . . . . . . . . . . 15
2.4.10 Clique Level (KL) . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.5 Methods for Essential Protein Prediction . . . . . . . . . . . . . . . . 16
2.5.1 Score Ranking . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.5.2 Prediction by Classifiers . . . . . . . . . . . . . . . . . . . . . 17
Chapter 3. Our Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.1 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.1.1 Topological Properties . . . . . . . . . . . . . . . . . . . . . . 19
3.1.2 Bit String Implementation of Double Screening Scheme . . . . 22
3.1.3 Protein Properties . . . . . . . . . . . . . . . . . . . . . . . . 24
3.1.4 Sequence Properties . . . . . . . . . . . . . . . . . . . . . . . 25
3.1.5 Other Properties . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.2 Our Method with SVM . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Chapter 4. Experimental Results . . . . . . . . . . . . . . . . . . . . . . 31
4.1 PPI Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.2 Data Balancing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.3 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.4 Experimental Results and Comparison . . . . . . . . . . . . . . . . . 33
Chapter 5. Conclusion and Future Work . . . . . . . . . . . . . . . . . 45
BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

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