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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 賴飛羆 | zh_TW |
| dc.contributor.advisor | Fei-Pei Lai | en |
| dc.contributor.author | 鄭佳芳 | zh_TW |
| dc.contributor.author | Chia-Fang Cheng | en |
| dc.date.accessioned | 2023-08-08T16:29:15Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-08-08 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-07-17 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88142 | - |
| dc.description.abstract | 本研究提出了一項對於預測人類粒線體核糖體DNA變異致病性的綜合分析。我們提出了一種基於機器學習極限梯度提升加上特徵整合的新方法，該方法集成了多個因素，包括同質性、異質性、等位基因頻率、異質性程度、變異導致的良性或致病性變化率，通過核苷酸突變熵計算的核苷酸突變的可變性與複雜性，以及核苷酸突變導致的序列信息改變（例如結構變化、酮基氨基存在等），並通過SHAP找出模型預測致病性所判定的特徵重要度。目前尚未發表任何針對人類粒線體核糖體DNA的預測方法，我們的方法是第一個且在評估數據集上取得了0.9886的F1分數。通過利用機器學習的力量並考慮粒線體核糖體DNA的獨特特徵，我們的方法為準確預測粒線體核糖體DNA變異的致病性提供了一個有價值的工具。 | zh_TW |
| dc.description.abstract | This study proposes a comprehensive analysis for predicting the pathogenicity of human mitochondrial ribosomal DNA (mt-rDNA) variations. We introduce a novel approach based on XGB model with feature integration, which integrates multiple factors including homogeneity, heterogeneity, allele frequency, heteroplasmy level, variation-induced benign or pathogenic rate of change, variability and complexity of nucleotide mutations calculated through nucleotide mutation entropy, and sequence information alterations caused by nucleotide mutations (such as structural changes and presence of keto-amino bases). Additionally, we utilize SHAP (Shapley Additive Explanations) to identify feature importance in determining the pathogenicity predicted by the model. Currently, no prediction methods specifically targeting human mt-rDNA variations have been published, and XGB with feature integration is the first to achieve an F1 score of 0.9886 on the evaluation dataset. By harnessing the power of machine learning and considering the unique characteristics of mt-rDNA, our approach provides a valuable tool for accurately predicting the pathogenicity of mitochondrial ribosomal DNA variations. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-08T16:29:15Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-08-08T16:29:15Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii Acknowledgment iii 中文摘要 iv Abstract v CONTENTS vi LIST OF FIGURES ix LIST OF TABLES x Chapter 1 Introduction 1 1.1 Background and motivation of the study 2 1.2 Research Objectives and Significance 2 1.3 Research Questions and Hypotheses 3 1.4 Brief Overview of Research Methods 3 Chapter 2 Mitochondrial DNA and Pathogenicity 5 2.1 What Is Mitochondrial DNA 5 2.2 The Functions of Different Segments of Mitochondrial DNA 6 2.3 Why and How Mutation of Mitochondrial DNA Will Cause Disease 6 2.4 Mutation Types of Mitochondrial DNA 7 2.5 Our Target in this Research 8 Chapter 3 Machine Learning Techniques 9 3.1 What Is Machine Learning? 10 3.2 The Typical Procedure of Machine Learning 10 3.3 Preprocessing 11 3.4 Model Training 12 3.4.1 Model types 13 3.4.2 Hyperparameter tuning 14 3.5 Evaluation 15 3.5.1 Cross Validation 15 3.5.2 Accuracy, Precision, Recall, F1 Score 16 3.5.3 AUC Curve 17 3.5.4 SHAP (Shapley Additive Explanations) and feature importance 17 Chapter 4 Related Works: ML and Pathogenicity Prediction 19 Chapter 5 Proposed Method 22 5.1 Method Overview 22 5.2 Dataset Collection 23 5.2.1 Database 23 5.3 Dataset Preprocessing and Merge 25 5.3.1 Feature Selection: 25 5.3.2 Imputation 27 5.3.3 Oversampling 27 5.3.4 One-Hot Encoding Transformation 28 5.3.5 Dataset Merge 29 5.4 Feature Integration 30 5.5 ML Techniques 36 5.5.1 Randomized Search 36 5.5.2 XGB Model: 37 5.6 Model Evaluation 38 5.7 Make Prediction on Unknown Label Data 39 Chapter 6 Experimental Results 40 6.1 Baselines 40 6.2 Numerical Result 45 6.3 SHAP values and feature importance 48 Chapter 7 Conclusion 55 REFERENCE 57 Appendices 62 | - |
| dc.language.iso | en | - |
| dc.subject | 極限梯度提升 | zh_TW |
| dc.subject | 人類粒線體核糖體DNA | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 特徵整合 | zh_TW |
| dc.subject | 致病預測 | zh_TW |
| dc.subject | pathogenicity predictor | en |
| dc.subject | XGB | en |
| dc.subject | Human mitochondrial ribosomal DNA | en |
| dc.subject | machine learning | en |
| dc.subject | feature integration | en |
| dc.title | 以機器學習方法預測人類粒線體核糖體DNA變異之致病性 | zh_TW |
| dc.title | Using machine learning methods to predict the pathogenicity of human mitochondrial ribosomal DNA mutations | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.coadvisor | 李妮鍾 | zh_TW |
| dc.contributor.coadvisor | Ni-Chung Lee | en |
| dc.contributor.oralexamcommittee | 莊志明;曾新育;張哲瑋 | zh_TW |
| dc.contributor.oralexamcommittee | Jyh-Ming Juang;Hsin-Yu Tseng;Che-Wei Chang | en |
| dc.subject.keyword | 人類粒線體核糖體DNA,機器學習,極限梯度提升,特徵整合,致病預測, | zh_TW |
| dc.subject.keyword | Human mitochondrial ribosomal DNA,machine learning,XGB,feature integration,pathogenicity predictor, | en |
| dc.relation.page | 62 | - |
| dc.identifier.doi | 10.6342/NTU202301584 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2023-07-17 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | - |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
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