基因調控網路研究是一項跨領域學門，它結合了微陣列技術與計算方法。如果調控關係發生問題，則可能引發疾病，因此，本研究的第一部份透過演算法分析微陣列資料，試圖找出與癌症相關的基因調控模組，了解這些參與在細胞週期中的基因如何被調控，分析哪些轉錄因子(transcription factors)為主要調控細胞週期現象，並協助了解在細胞週期中的致癌基因與其它基因之間的調控關係。
從後轉錄調控的層次來看，微型核糖核酸(MicroRNA)掌握著對目標mRNA的後轉錄調控機制，導致轉譯作用的抑制或mRNA的降解 。本研究第二部份利用物種間序列演化保留的特性，以及二級結構中的一些特徵，預測人類的微型核糖核酸可能辨識並黏合的目標mRNA。
本研究第三部份則是提出一針對RNA二級結構相似度比較，與相似子結構採掘的方法，藉由比對RNA二級結構來了解或預測non-coding RNA的功能。本論文試圖透過演算法與電腦資訊的技術，分析探討生物類型的資料，期待未來此方法可以很容易的應用到其他生化反應，進一步深入的了解生物現象。

Modeling cancer-related regulatory modules from gene expression profiling of cancer tissues is expected to contribute to our understanding of cancer biology. In this study, we introduce a GA-RNN hybrid method to construct cancer-related regulatory modules in human cancer microarray data.
As for the post-transcriptional regulation level, most of microRNAs are thought to control post-transcriptional mechanism by base pairing with microRNA recognition elements found in their mRNA targets. A computational method we provide is to predict mRNA targets for microRNAs by combining structure-based features and conserved data across species.
Finally, a graph-based approach for comparing RNA secondary structures is introduced to imply or predict the functions of non-coding RNAs. We have proved the performance of the proposed algorithm by providing the experimental results. While the proposed algorithm has been designed for use in detection of common RNA secondary structures, it may be applicable to other graph-based similarity applications, such as protein structure problems.

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