miRNA為長度約20-25nt的非編碼RNA，在生物體內扮演轉譯調控因子，可以調節或抑制蛋白質的表現，近年來被發現與一些癌症及疾病的機轉有關，因此尋找miRNA的標靶物成為近幾年熱門的研究之一；本研究為單一miRNA設計一套流程，將特定單一miRNA過度表現情況下的mRNA microarray作為輸入資料，根據目前已知被證實的標靶，經過支持向量機的訓練來達到判斷是否為標靶的分類效果，並加入物種保留性的概念，在人與老鼠之間留下共同，以過濾現有的多半以序列比對、能量自由度及結構等資訊做為篩選條件的miRNA標靶預測工具所預測出的結果，達到降低假陽性的目的；而研究結果所篩選出的基因，經過疾病通路比對，最低超過35%之路徑可得到過去現有的文獻支持，而能達到降低假陽性的目的。

MicroRNAs are a class of small non-coding RNAs which play important regulatory roles in animals and plants. They cause transcriptional cleavage or translational repression through binding their target mRNAs. MicroRNAs affect a variety of cellular processes such as development, cell proliferation, apoptosis, and stress response. Thus identification of mRNA targets is an essential step to understand microRNA functions.
Currently several microRNA target prediction tools have been developed. The majority of these algorithms are based on the sequence alignment or the minimum free energy of the hybridization. However, due to the omission of gene expression information in the screening process, a number of candidate targets could be false positives which are too large to validate.
In this work, a filtering strategy which was implemented based on SVM machine learning has been built in order to reduce the false positives. Information of sequence alignment retrieved from existing database and microarray expression data were both used to classify mRNA candidates into non-target or target group by SVM, trying to separate microRNA target genes from non-target genes. Besides, the concept of conservation between species has been included to mitigate the problem of noisy data then decrease false positive predictions.

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