| Summary: | 碩士 === 國立臺灣大學 === 生醫電子與資訊學研究所 === 97 === MicroRNAs (miRNAs) are short non-coding RNAs about 22 nucleotides that play important regulatory roles in animals for translational repression. Nevertheless, it is a difficult challenge to predict targets in animals because of their much more imperfect complementarity between microRNAs and mRNAs. In order to further improve the prediction performance, we propose a novel microRNA target-gene prediction algorithm which combines several conventional prediction models such as the sequence complementary searching for calculating alignment scores and thermodynamic stability approaches for assigning folding free energy to each microRNA-target interactions. Besides, it includes a Hidden Markov Model (HMM), which is a famous machine learning approach, to help the prediction decision. However, due to its innate limitation, HMM can’t consider all the global information of the sequences. Hence, in order to overcome this limitation, forward and backward HMMs are simultaneously utilized in the proposed algorithm. As a result, it can make any element information of microRNA-target interactions able to pass to any other element by bi-directions.
In this thesis, the author calculates the highest sensitivity, specificity, and overall accuracy in the different combination of the proposed models. And it also uses the predicted genes from existing prediction algorithms and down-regulated genes from microarray data to demonstrate the correctness of the proposed algorithm. According to the simulation result, the corresponding sensitivity, specificity, and overall accuracy are 84.25%, 96.78%, and 96.67%, respectively in the complete prediction models. And it is determined that 52.42% and 70.37% overlap rates predicted by the proposed algorithm also can be estimated in other existing prediction algorithms and the down-regulated results of microarray data, respectively.
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