在人類基因組計劃揭開基因序列後，單倍體是一種對於遺傳疾病研究、藥物目標預測研究上都非常具有助益的資訊，但要用臨床實驗的方法得到單倍體的資訊是非常耗時間而且在經濟上也是一大考驗，所以近年來越來越多的研究是想要從電腦模擬推斷出單倍體的資訊，希望藉由這種方式去加快得到單倍體的資訊。在這篇論文裡，推斷正確單倍體資訊的問題中，我們提出藉由導入多個單核苷酸多態性之間的相互關聯性資訊到成長樹的解決方式裡，經過這樣的方法，我們確實在推斷準確率上得到相較於簡約樹成長法最高至2.62%的成長，且在推斷正確單倍體的問題上，最高的推斷準確率達到95.2%。藉由高準確率的推斷單倍體後，我們希望可以跳過臨床實驗去取得單倍體資訊的步驟，直接拿來幫助其他相關研究。

Haplotypes are a kind of powerful information that is helpful in gene candidate studies because of inheritance characteristics. However, in order to get the haplotype information, in vitro methods cost lots of time and money, it is helpful to infer haplotypes using in Silico methods. Because the haplotype inference is a NP-Hard problem, both the accuracy and computational time are important issues. In this thesis, we take into account of the normalized mutual information in the parsimonious tree-grow methods that show very good performance on haplotype inference problems. And we improve the inference accuracy rate most to 2.62 percent on APOE gene dataset which just spend about 0.001 more seconds than original parsimonious tree-grow method. We also have the highest to 95.2% accuracy rate on β2AR gene data in comparison to previous approaches.

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