| Summary: | 碩士 === 國立中興大學 === 基因體暨生物資訊學研究所 === 98 === In the current cancer research field, microarray is one of the most commonly used tools. It has the advantage of containing a large amount of data, which helped us in recording gene expressions in cancer and comparing the difference between normal cells and cancer cells. However, contemporary cancer research does not have a positive definition in how to analyze the microarray data. In this essay, we utilize the microarray data from the carcinoma cancer as primary sample. And we apply statistical methods and mathematical calculations to establish a diseases analyzing model. At first, we use principal component analysis to process the pre-selected data. Then we use ANOVA to select the genes with significant expression differences to be our target genes. Finally we use supervised learning method to evaluate the accuracy of classification. If the accuracy of target genes is higher than the threshold we set, we apply Dijkstra algorithm and Bayesian theorem to construct the gene regulatory network for these genes. It can provide the researchers with a better understanding in the functionality and regulated directions of these genes. This analytical model helps to reduce the number of incorrect attempts and cut down the time which has to be spent in experiments. This model can analyze complicated cancer expression data, and it can also be useful in researches for other diseases. In prospect, this analytical method can be used in medicine development, for discovering more efficient treatments.
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