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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 趙坤茂(Kun-Mao Chao) | |
| dc.contributor.author | Pei-Chen Peng | en |
| dc.contributor.author | 彭姵晨 | zh_TW |
| dc.date.accessioned | 2021-06-16T13:27:05Z | - |
| dc.date.available | 2016-07-31 | |
| dc.date.copyright | 2013-07-31 | |
| dc.date.issued | 2013 | |
| dc.date.submitted | 2013-07-23 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/62088 | - |
| dc.description.abstract | 基因表現量與基因型資料近年來成指數成長，為了廣泛運用此類型資料於各項研究，統整分析遺傳基因體資料，並尋找顯著表現基因，已成為目前的趨勢。
此研究提出一套簡單的遺傳基因體資料分析流程，首先整合基因表現量與基因型資料,接著以隨機森林演算法進行特徵選取。這套流程被應用於兩個項目: 推導基因調控網路及預測複雜表現型。十五個分別含有一千個基因的基因調控網路被推導，我們以接收操作特徵曲線下的面積及精確與檢索率曲線下面積來評量推導結果。關於預測複雜表現型方面，此套流程可被用來預測大豆的抗病能力，我們以斯皮爾曼等級相關係數來評量預測結果。實驗結果顯示，不論在模擬或真實的遺傳基因體資料，此套分析流程的效果都優於其他方法。整合基因表現量與基因型資料是分析遺傳基因體資料的關鍵步驟。此外，隨機森林演算法是一個找出顯著表現基因的理想方法。 | zh_TW |
| dc.description.abstract | The amount of gene expression pro ling and genotype data have grown exponentially. To apply them for extensive studies, integrated analysis of genetical genomics data becomes a trend and thus identifying relevant genes of specifc response is an essential issue. We propose a simple workflow for genetical genomics data analysis which includes integration of genotype and gene expression data as well as Random Forest feature selection. The proposed workflow is utilized in two applications: gene regulatory network inference and complex phenotype prediction. Fifteen different gene networks composed of one thousand genes respectively are reconstructed. Area under Receiver Operator Characteristic curve and Precision-Recall curve are measured for inference performance. For the other application, disease susceptibility of soybean plants are predicted. Spearman's rank correlation coefficient is used for prediction evaluation. Results show that our method outperforms other methods in both simulated and real genetical genomics data. Integration of genotype and gene expression is a pivotal step in genetical genomics data analysis. And Random Forest is an ideal way to find out relevant genes for further applications. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T13:27:05Z (GMT). No. of bitstreams: 1 ntu-102-R00922010-1.pdf: 1119431 bytes, checksum: 7745983f7a61b52e7e245bfde7f3f338 (MD5) Previous issue date: 2013 | en |
| dc.description.tableofcontents | 口試委員會審定書 iii
致謝 v 摘要 vii Abstract ix 1 Introduction 1 1.1 Feature Selection in Bioinformatics . . . . . . . . . . 2 1.2 Gene Regulatory Network . . . . . . . . . . . . . . . . 3 1.3 Complex Phenotype Prediction . . . . . . . . . . . . . 5 1.4 Workflow of Genetical Genomics Data Analysis and Its Applications . . . . . . . . . . . . . . . . . . . . . . . 6 2 Genetical Genomics Data Analysis 9 2.1 Genetical Genomics . . . . . . . . . . . . . . . . . . . 9 2.2 Integration of Genotype and Gene Expression Data . . 11 2.3 Feature Selection by Random Forest . . . . . . . . . . 13 3 Gene Regulatory Network Inference 17 3.1 Genetical Genomics Data Collection . . . . . . . . . . 17 3.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.2.1 Network Inference as a Feature Selection Problem 18 3.2.2 Genetical Genomics Data Analysis . . . . . . . 19 3.2.3 Ranked Edge List . . . . . . . . . . . . . . . . 20 3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.3.1 Evaluation of Performance . . . . . . . . . . . 21 3.3.2 Assessment of Each Network Inference . . . . 22 4 Complex Phenotype Prediction 29 4.1 Genetical Genomics Data Collection . . . . . . . . . . 29 4.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . 31 4.2.1 Genetical Genomics Data Analysis . . . . . . 31 4.2.2 Support Vector Regression . . . . . . . . . . . 32 4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.3.1 Evaluation of Performance . . . . . . . . . . . 33 4.3.2 Assessment of Predictions . . . . . . . . . . . 33 5 Concluding Remarks 37 5.1 Genetical Genomics Data Analysis Performs Well in Simulated and Real Data . . . . . . . . . . . . . . . . . . . 37 5.2 Integration of Genotype and Expression Data is Pivotal 38 5.3 Random Forest is Suitable for Genetical Genomics Data Feature Selection . . . . . . . . . . . . . . . . . . 39 Bibliography. . . . . . . . . . . . .. . .. . 41 | |
| dc.language.iso | en | |
| dc.subject | 遺傳基因體 | zh_TW |
| dc.subject | 複雜表現型預測 | zh_TW |
| dc.subject | 遺傳基因體 | zh_TW |
| dc.subject | 基因調控網路推導 | zh_TW |
| dc.subject | 複雜表現型預測 | zh_TW |
| dc.subject | 基因調控網路推導 | zh_TW |
| dc.subject | Genetical genomics | en |
| dc.subject | Complex phenotype prediction. | en |
| dc.subject | Gene regulatory network reconstruction | en |
| dc.subject | Genetical genomics | en |
| dc.subject | Complex phenotype prediction. | en |
| dc.subject | Gene regulatory network reconstruction | en |
| dc.title | 以遺傳基因體資料推導基因調控網路及預測複雜表現型 | zh_TW |
| dc.title | Gene Regulatory Network Inference and Complex Phenotype Prediction from Genetical Genomics Data | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 101-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 王弘倫(Hung-Lung Wang),陳怡靜(Yi-Ching Chen) | |
| dc.subject.keyword | 遺傳基因體,基因調控網路推導,複雜表現型預測, | zh_TW |
| dc.subject.keyword | Genetical genomics,Gene regulatory network reconstruction,Complex phenotype prediction., | en |
| dc.relation.page | 47 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2013-07-23 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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