微核糖核酸(miRNAs)是小片段不會轉譯出蛋白質的核糖核酸(RNA)，透過轉譯的抑制(translation inhibition)或傳訊核糖核酸(mRNA)的降解(degradation)來調控標的基因(target genes)的表現。在分析微陣列(microarray)資料中，我們發現當miRNA有表現量上的顯著改變時，並不是其所有targets都會有表現量改變，因此如何辨別miRNA的活性是我們的研究主題。在本論文中，當一個miRNA所有targets的表現量變化大於其非目標基因(non-targets)的表現量變化，我們定義該miRNA具有活性。過去已有許多關於如何利用mRNA表現量資料來辨別miRNA的活性研究，例如TREX及mirAct等。但我們測試了幾組資料後發現他們的預測結果不如預期。為了改善預測的結果，我們首先找到較適合用來預測miRNA活性的miRNA targets資料庫(miRNA target prediction)。利用同時具有mRNA與miRNA的表現量資料，來計算miRNA與target間表現量的相關係數(correlation)。我們發現相較於其他資料庫，利用Pictar可得到較強的負相關性，也就是Pictar是一個較適合用來預測miRNA活性的miRNA targets資料庫。另外我們也發現隨著樣品數目(sample size)的增加，miRNA與targets間的負相關性趨勢越來越顯著，然而儘管搭配Pictar與大樣品數兩種特性進行預測，其預測結果仍具有高偽陽率(high false positive rate)。故我們認為利用miRNA表現量資料來加強預測是必須的，透過miRNA表現量資料可以去除預測出具活性但實際上沒有表現量改變的miRNA，更加確認miRNA是否具活性。此外，我們發現利用個體差異的預測方法可以改善活性預測的結果，即當mRNA表現量資料可分為兩群，例：腫瘤組與對照組，我們將各別腫瘤組的樣品之表現量與所有對照組比較，可預測並得到該腫瘤組中具活性的miRNA。由於個體間差異變化之大，故此預測miRNA活性的方法未來或許可以應用於個人化醫療。

MicroRNAs (miRNAs) are small non-coding RNAs that regulate target genes’ expression through translation inhibition or mRNA degradation. In microarray data, we observed that not all the target genes of a miRNA are up- or down-regulated when the miRNA is differentially expressed. Therefore, to identify miRNA’s activity is an important topic. Here we defined that a miRNA is active if its target genes are more up- or down-regulated than its non-target genes. A number of computational approaches, e.g., TREX and mirAct, have been developed to predict miRNA’s activity using mRNA array data. However, we tested TREX and mirAct on many mRNA array data sets and found that the performances were not good. To enhance the performance, we set out to find the target prediction program that gives the strongest negative correlation in expression between microRNAs and the predicted targets. Using the data sets with both microRNA and mRNA expression, we found that the miRNA and target gene pairs predicted by Pictar were more negatively correlated compared with other target prediction programs. We also observed that the larger sample size, the stronger negative correlation between miRNAs and their target genes. Nevertheless, even with PicTar and a large sample size, the false positive rate is still not satisfactory. Thus, it is necessary that we incorporate microRNA’s expression data to filter away the microRNAs predicted as up- or down-regulated but in fact not differentially expressed. In addition, we found that microRNA activity prediction is better when we consider individual differences. When two groups of samples are involved, e.g., normal v.s. tumor, we obtained the differential expressions of genes by comparing the expressions between the two groups. We found that if we treated each tumor sample separately, i.e., by comparing the gene expression in one tumor sample to those of the normal samples, individual microRNA activity can be better predicted. As individual differences are relatively large, this approach provides personal information of microRNA activity, which could be useful for personalized medicine in the future.

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