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研究生:鄒友翔
研究生(外文):Yu-Hsiang Tsou
論文名稱:整合多種基因組型態資料預測肺腺癌患者存活之研究
論文名稱(外文):Integrative analysis of multiple genomic data types to predict the survival in lung adenocarcinoma
指導教授:許藝瓊許藝瓊引用關係
指導教授(外文):Yi-Chiung Hsu
學位類別:碩士
校院名稱:國立中央大學
系所名稱:系統生物與生物資訊研究所
學門:生命科學學門
學類:生物訊息學類
論文種類:學術論文
論文出版年:2018
畢業學年度:107
語文別:中文
論文頁數:53
中文關鍵詞:肺腺癌基因定序生物統計深度學習機器學習
外文關鍵詞:lung adenocarcinomamultiple genomic databiostatisticaldeep learningmachine learning
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目前高通量定序資料已廣泛的運用在肺腺癌的研究中,包括表觀基因組學,基因體學和轉錄組學等等;全面分子特徵有助於確定生物標誌物以及用於早期診斷與治療。因應精準醫療的臨床需求,目前次世代定序隨著高通量定序品質提高以及成本的降低,使得其應用在臨床的可行性大幅提高。本篇論文使用基因體圖譜計畫(TCGA)中的肺腺癌基因組數據。我們採用生物統計分析以及深度學習的方法,分析TCGA肺腺癌多種基因組數據。我們整合TCGA肺腺癌372位病人包括RNA、miRNA、甲基化基因組數據,建立預測因子模型來預測肺腺癌高風險和低風險患者疾病進展存活。在統計分析中找出十七基因印記可顯著預測患者疾病進展存活,並在另外三組肺腺癌病人中驗證所找出的基因印記能準確預測存活。在深度學習分析方面,我們透過自編碼器深度學習模型建立預測模型並在驗證組上有良好的預測效果。
High-throughput genomic assays have been widely used to investigate lung adenocarcinoma by employing technologies of epigenomics, genomics and transcriptomics. Comprehensive characterization of molecular mechanisms has contributed to identify biomarkers for early diagnosis and treatment. Nowadays, high-throughput sequencing technology providing a higher coverage and lower cost is prevalent in clinical application. This study used biostatistical and deep learning-based computing methodologies to conduct data mining and modeling on adenocarcinoma lung cancer datasets extracted from TCGA data. We integrated RNA, miRNA, and DNA methylation genomic data from 372 lung adenocarcinoma patients. Based on biostatistical methods, we developed a 17-gene signature to distinguish the risk groups with disease-free survival. In addition, we validated a 17-gene signature in three independent lung adenocarcinoma cohorts. Besides statistical validation, we also used Deep Learning-based autoencoder modeling to validate these datasets.
中文摘要 i
Abstract ii
致謝 iii
一、緒論 1
1-1 研究背景、動機及目的 1
1-2肺腺癌(Lung Adenocarcinoma) 2
1-3目前肺癌治療方式 3
二、研究材料與方法 4
2-1 研究材料 4
2-1-1 癌症基因體圖譜(The Cancer Genome Atlas, TCGA) 4
2-1-2 基因表達資料庫(Gene Expression Omnibus , GEO) 4
2-2 研究方法 6
2-2-1 分析環境與套件 6
2-2-2 資料前處理 6
2-2-3 存活分析 7
2-2-4 斯皮爾曼等級相關係數 10
2-2-5 miRTarBase資料庫 10
2-2-6 自動編碼器(Autoencoder) 10
2-2-7 K-means分群法 11
2-2-8 變異數分析(Analysis of variance, ANOVA) 11
2-2-9 支援向量機(Support vector machine, SVM) 11
三、結果 12
3-1 生物統計分析 12
3-1-1 基因篩選 12
3-1-2 基因印記 15
3-1-3 驗證基因印記在驗證組中預測疾病存活復發 18
3-1-4 基因印記分類 21
3-1-5 Gene ontology分類 24
3-1-6 小結 25
3-2 深度學習分析 26
3-2-1 存活風險分群 26
3-2-2 在TCGA肺腺癌多基因組學中找出差異顯著的分群 28
3-2-3 支援向量機模型預測驗證組資料存活風險分群 28
四、討論 32
4-1 生物統計及深度學習所篩選基因交集 32
4-2 生物統計及深度學習所篩選miRNA交集 34
4-3 在生物統計及深度學習結果同為高風險或為低風險分群以及不同分群 34
4-4 分析方法比較 37
五、結論 38
六、參考文獻 39
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