微陣列(Microarray)資料提供了生物領域的學者，一些有用的參考價值。但微陣列資料通常會在實驗的過程中，因為人為操作的不當或者是晶片本身的缺陷，進而導致微陣列資料產生了缺失值(Missing Values)，而缺失值的存在會影響到生物學家在接下來資料的分析上產生問題，所以在進行分析真正資料之前，都會先估測出其缺失值。
大概在十年前出現了第一個估測的方法，從此之後各式各樣的新方法就快速的成長，而每一種方法其會因資料來源的不同而有不同的表現。而關於缺失值的估測，主要有兩個步驟，第一個是找出k個與要估測的基因最相似的基因群，第二就是利用這些基因群結合演算法來估測出缺失值。
而本論文主要是提供一個新的估測演算法，主要也是分成兩個步驟，第一步利用向量角度與長度的觀念找最相似的k個鄰居，第二步則是利用權重最小平方法來估測出缺失值。接著再進行幾種不同的微陣列資料，結果顯示我們所提出的方法都比其他方法更準確或者是相差不遠。

Microarray data provided some useful information for biologists. However, many microarrays suffer from missing values, due to human factors or defects in the chip itself. Missing values will affect biologists doing the following analysis, so currently there are many estimation method for solving the problem. Before we do the analysis with the real gene microarray data, we will estimate the missing values firstly.
Since the first method was introduced nearly a decade ago, new and improved methods have been developed continuously. However, every method has different performance according to different data resource. The way of estimating missing values is commonly divided into two steps. The first is to choose the most similar k-th genes with the target gene; the second is to use these genes combined with some least-squares algorithms to estimate missing values.
This thesis introduces a new estimation method which is also divided into two steps. First, we use the concept of vector's angle and length to choose the most similar k-th neighbors. Second, we use the weighted least-squares method to estimate the missing values. Several different microarray datasets are used to verify the proposed method and we also make comparison with other algorithms. The results show that our method is more accurate than the other methods, or not far.

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