基因微陣列(Microarray)是近年來基因體生物醫學研究的新技術。其產生的龐大資料需要應用到統計分析及生物資訊的相關方法，期望能找出具有生物意義的結果，而這也是當前生物資訊研究者的熱門研究課題。本研究以傅立葉模型(Fourier model)配適多時期微陣列基因資料，以AIC準則選取每一個基因相對應的項數和週期，再針對所得到的係數值、項數和週期進行資料的模型插補，並以傅立葉模型配適後的項數(term)和週期(cycle)來預測。經由模擬發現，我們所用的方法無論在資料配適和預測上，有較佳的效率。

In recent years, the development of cDNA microarray technology allows people to investigate thousands of genes simultaneously. An enormous number of gene expressions are generated through the high throughput technology, hoping that biological information can be extracted by the use of statistical analysis. This thesis is devoted to applying Fourier model to fit time-course microarray gene expression data, selecting the term and cycle for each gene by AIC criteria. The efficiency in estimating the term and cycle, and the accuracy in estimation and prediction in interpolating some time points are investigated. It is found that, through simulation, the data interpolation approach is able to improve the efficiency in estimation and prediction.

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