質譜分析在蛋白質體學研究中是重要的技術之一，而質譜資料前處理過程中又以基底線修正及波峰校準處理更是影響最後分析結果品質的關鍵。在目前已有的研究方法中，往往基底線修正的失真度與波峰校準的雜訊敏感度皆過高。因此，在本研究中，我們分別提出改進方法。在基底線修正的處理上，我們結合凸包(convex hull)演算法和LOESS迴歸法的優點找出更精準的質譜基底線，如此便能提升質譜訊號的品質。另一方面，由於目前已有的波峰校準方法無法找出雜訊位置，因此做出來的校對結果容易受到雜訊影響，所以我們提出了一個新的波峰校準演算法TPC (Two-Phases Clustering)，利用此演算法，我們可以有效地從含有雜訊的波峰集中，把潛在雜訊從中篩選出來，進而提升質譜波峰資料間校對的正確性。在實驗部份，我們使用真實資料與人造資料來測試效能。在真實資料的實驗結果中，其效能評估比之前的方法還要好，而在人造資料的實驗中，我們所提出的方法可以更精確的找出實驗預藏的潛在雜訊，並且其涵蓋率(Recall)、精確率(Precision)以及F-measure值都很高。由實驗結果來看，我們提出的方法的確比目前已有的分析法有更佳的正確性。

In most proteomic studies, Mass spectrometry (MS) data analysis has become an important protein identification technique. The “baseline correction” and “peak alignment” methods are the key factors in MS data preprocessing stage for further analysis. However, the existing baseline correction methods may cause the distortion for original peak signals. And the existing peak alignment methods may be sensitive to noise peaks across various MS samples. In this study, we proposed two novel algorithms for these two key factors. We combined Convex Hull algorithm and LOESS regression method to find a better baseline for a MS data. It can successfully correct each MS peak profile and the result is more similar to original profile than the existing methods do. In the existing peak alignment methods, no studies have ever tried to point out the inconsistent peaks across various MS samples. We also proposed a new TPC (Two-phases clustering) algorithm to align multiple MS samples while the potential noise peaks could be indexed. In our experiments, we used real MS datasets and also generated synthetic datasets to evaluate the accuracy of peak alignment method. The results show that our method is better than previous method.

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