DNA 微陣列晶片可以一次同時觀察到數千至上萬個基因。因此，
這項技術被廣泛的使用在臨床實驗中，尤其在搜尋癌症的致病基因
上。所以近年來有大量關於這些研究的報告，但這些報告所搜尋出的基因卻無法適用在大部分的人身上。主要理由是因為許多研究者都忽略一些潛在而易犯的錯誤，如蒐集資料的方式、樣本間基本資料的齊一性等，使得研究結果受到質疑。因此，這篇論文主要是提供非統計學家基本資料分析的步驟，將其應用在MALDI 資料上(蛋白質晶片)來搜尋致病基因。最後，我們將此步驟套用在由范德堡大學石瑜教授所提供的肺癌資料上，將其基因表現值透過基準點的調整後，使用四種分類器以及八次的交叉驗證，篩選出四個基因可以用以區別肺癌，為X2016, X2035, X2056, 和 X2082，且分類準確率達97%。

The expression of several thousand genes can be studied simultaneously by DNA microarrays. It is a highly promising technique with broad applications, especially
applies prognostic prediction at present. Therefore, the literatures are abundant about prognostic markers for patients with cancer. But most proposed markers are false
alarm. Because of many potential pitfalls exist in the microarrays data, for example the method of collecting data is not clear, the baseline of data does not consistent etc. The majority researchers leave it out of consideration, therefore the result usually is
queried. This paper provides fundamental statistical analysis based on the matrix-assisted laser desorption ionization (MALDI) mass spectrometry data provided by Dr.
Yu Shyr of Vanderbilt University. We analyze the lung cancer data using subjectbackground data to adjust the intensity data of mass spectrometry. Four features are
found including X2016, X2035, X2056, and X2082 to distinguish lung cancer with classified accuracy of 97% by the support vector machine classifier and 8-fold cross
validation.

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