隨著人類基因體的定序完成，蛋白質體學現已成為二十一世紀最熱門的研究主題，研究人員目前常透過質譜儀探究基因密碼和不同蛋白質間的關係，希望能進一步了解生物體的致病原因和疾病的預防。然而在執行質譜儀時，所產生的蛋白質圖譜可能因為儀器的誤差、或者不同的實驗時間，造成圖譜中的波峰產生位移，這個波峰位移現象讓研究人員無法輕易地辨別同種蛋白質波峰在不同的質譜樣本中的位置，導致後續分析蛋白質體序列時的困難度。所以在本篇論文中，我們著重於波峰校正方法改良，預計解決蛋白質圖譜中「波峰位移」的問題，期望透過我們所提出的方法，將實驗過程中波峰產生的潛在位移誤差移除並且波峰位置能夠調校回正確數值，更精準地讓不同樣本中的波峰校正在一起，方便研究人員在後續的蛋白質分析。本篇論文提出PeakAlign Algorithm，首先使用了局部加權迴歸分析平滑方法(LOESS Regression Method)將圖譜中的誤差值去除，接著藉由改良式的群集演算法將相似度高的波峰群集在一起。在實驗的部分，我們使用真實資料與人造資料作為效能測試的依據，從真實資料的實驗結果中，我們可以發現若是質譜中的波峰有經過位移調整並且移除其誤差值，那麼在做波峰校正時可以更為精準。另外無論是在真實資料或者是人造資料的實驗中，我們提出的PeakAlign Algorithm方法的準確率表現皆優於其他常用的演算法，例如：DTW和SlidingWin 演算法。

Identifying proteomics markers to classify diseases by using mass spectrometry with high-performance liquid chromatography (LC-MS) has been a trend recently. However, since the experimental errors, one traditional problem (also known as the peak-shifting problem) occurred in the preprocessing of mutltiple LC-MS data analysis is that the identical peptides from multiple samples may have different retention time drifts. In our study, we proposed an algorithm, namely PeakAlign, to solve the peak-shifting problem. Our algorithm consists of two phases, the adjustment phase and the alignment phase. In the adjustment phase, the LOESS regression method is used to calculate the different shifting values among peaks along the retention time. In the alignment phase, a novel cluster-based technique based on the distance constraint is applied to align the adjusted peaks. To evaluate the PeakAlign, we used two real LC-MS datasets as well as a set of generated semi-synthetic datasets to evaluate the accuracy and similarity of the alignment results. The experimental results show that the performance of our algorithm is much better than that of other methods, such as the DTW and the SlidingWin algorithms.

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