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研究生(外文):Hung-Ching Sung
論文名稱(外文):AryNet- Web App for detecting the relationships between Mental Diseases and Chemicals through Gene Network Analysis based on Microarray data
指導教授(外文):Sher Singh
外文關鍵詞:biochipmicroarraygene networkchemicalmental disorder
  • 被引用被引用:0
  • 點閱點閱:183
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  • 下載下載:22
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As the Internet develops, microarray data shows a powerful potential for detecting the novel genomic interaction with its powerful repeatability and reproducibility. There are a lot of web tools and open source packages for analysis and visualization of microarray data. But most of them are costly or too technical to use.
For the purpose of developing a tool to operate data processing, statistics, genomic comparison, and visualization on microarray data in a friendly usage. We built a web system named AryNet. Applying the MVC framework with an interactive controlling panel on web page, AryNet possesses a SQL database storing epigenetic and gene expression profiles of microarray data downloaded from GEO database including samples with mental diseases, neural diseases, and chemical exposure. The information of protein-protein interactions from Reactome is also installed. The Java-based controller was armed with a plugin named R-Engine to drive the R-package of data processing and statistics obtain from Bioconductor. The resulting analysis will be retrieved back to the user view and generates a gene networks diagram on time.
We obtain the differential expression genes (DEGs) profiles from bipolar disorder, schizophrenia, major depression and chemical exposure by AryNet. And then we generated the gene network diagrams combining the DEGs with genes which have relationships in protein-protein interaction of each profiles. Comparison with the gene networks shows that there might be some resemblance between the phenotypes of diseases and endocrine disruptors.
1 前言 1
1.1 研究動機 1
1.2 研究目的 2
2 文獻回顧 3
2.1 表觀遺傳(Epigenetics) 3
2.2 精神疾病(Mental Disorders) 7
2.3 環境賀爾蒙(Environmental Hormone) 9
2.4 生物途徑(Biological Pathway) 12
2.5 基因晶片(Gene Microarray) 14
2.6 基因網絡視覺化(Visualization by Gene Networks ) 17
2.7 R統計軟體(R Language) 20
2.8 MVC架站結構(Model-View-Controller) 21
3 研究方法 22
3.1 架設MVC資料庫 22
3.2 安裝R運算引擎套件 24
3.3 資料庫框架設定 25
3.4 資料庫來源 27
3.5 使用者操作介面與中央控制器設定 31
3.6 互動式網絡圖功能 33
3.7 系統運算流程 33
3.8 相關演算法實作與引用統計公式 39
3.9 環境賀爾蒙與精神疾病相關性分析 46
4 結果與討論 58
5 結論 59
6 未來研究方向 60
7 參考文獻 61

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