蛋白質交互作用在許多生物過程中扮演一個重要的角色。為了瞭解細胞內的分子機制，我們必須獲得允許蛋白質在細胞中執行多種工作的蛋白質交互作用的訊息。預測蛋白質交互作用在近幾年來已經變成熱門的議題，原因在於透過可靠的實驗來分析蛋白質交互作用是昂貴且耗時的。雖然大量的蛋白質交互作用資料已經被許多高速度的實驗技術產生出來，但是這些資料導致預測結果同時具有偽陽性以及偽陰性的機率很高。
近來的研究顯示，發展與應用蛋白質交互作用預測的計算機技術有重要的價值。然而，許多的預測系統需要有關蛋白質同源性的資訊以及/或蛋白質的結構資訊。因為可以取用蛋白質序列到的蛋白質資料來源較蛋白質結構資料來源來得豐富，所以一些只基於蛋白質序列資訊的蛋白質交互作用預測系統已經被開發出來。
在本研究中，我們提出一套黑腹果蠅的蛋白質間交互作用預測系統 (PPI-DC)。在我們的PPI-DC系統，雙肽組成(Dipeptide Composition)首先被用來將不同長度的蛋白質序列轉換成固定長度的數值陣列。第二步，基準線差值表示法(DLD)被提出來將蛋白質間交互作用對(pair)的數值陣列轉換成特徵向量。最後我們使用k-nearest neighbor 演算法來對蛋白質間交互作用進行分類預測。我們的系統在之前相關研究中使用的資料集上有更好的表現。實驗結果顯示運用了本論文所提出的方法的情況下，預測準確度上會有重要的改進。此外，我們的系統已經在不同的獨立資料集上被成功地的驗證蛋白質間交互作用預測的可靠度。

摘要 i
Abstract iii
第一章 1
導論 1
1.1 研究背景與動機 1
1.2 論文架構 6
第二章 8
相關研究 8
2.1 生物學的相關研究 8
2.2 其他種類的預測系統 9
2.3 和蛋白質交互作用有關的研究成果 10
2.3.1基於序列資訊的預測系統 10
2.3.2基於結構資訊的預測系統 11
2.3.3 基於領域資訊的預測系統 12
第三章 14
測試資料與系統架構 14
3.1 測試資料集合 14
3.1.1 蛋白質交互作用資料集合 15
3.2 系統Drosophila Melanogaster PPI-DC 簡介 16
3.3 常見的蛋白質描述方法 18
3.3.1蛋白質的胺基酸組成 (Amino Acid Compostion, AAC) 18
3.3.2偽胺基酸組成 (Pseudo-Amino Acid Composition, PseAA) 19
3.3.3 雙肽組成 (Dipeptide Composition, Dipeptide) 21
3.3.4結合胺基酸索引與分區段的AAwindow 22
3.3.4基於演化訊息的偽序列相似度方法 (Pseudo-Position Specific Scoring Matrix, PsePSSM) 24
3.3.5二變異的胺基酸組成 (Binary Variation Amino Acid Composition, BV-AA) 27
3.3.6基於演化訊息的二變異方法 (Binary Variation Method Based on Evolution Information) 29
3.4 蛋白質交互作用資料格式 32
3.5 蛋白質間交互作用對的特徵向量編碼 34
3.6 去除相似的蛋白質對 36
3.7 特徵抽取方法 38
3.8 最近鄰居演算法 (K-Nearest Neighbor, KNN) 40
第四章 41
實驗與分析 41
4.1 評估預測表現的方法跟標準 41
4.1.1 評估方法 41
4.1.2 預測表現的評斷標準 42
4.2 預測表現與系統比較 44
4.3 結論 50
參考論文 52

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