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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林澤 | zh_TW |
| dc.contributor.advisor | Che Lin | en |
| dc.contributor.author | 吳柏潤 | zh_TW |
| dc.contributor.author | Bo-Run Wu | en |
| dc.date.accessioned | 2023-09-15T16:18:52Z | - |
| dc.date.available | 2023-09-16 | - |
| dc.date.copyright | 2023-09-15 | - |
| dc.date.issued | 2022 | - |
| dc.date.submitted | 2002-01-01 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/89698 | - |
| dc.description.abstract | 癌症是全球主要的死亡原因之一，我們透過精準的癌症預後預測，篩選並給予高風險病患最合適的治療，提升病患的存活率。然而在現今複雜且龐大的異質醫學資料裡，深度學習相較於統計或傳統機器學習的演算法，在不同方面皆表現得更為出色，讓我們得以處理這些複雜且龐大的異質醫學資料。除此之外，在深度學習中，我們可以運用多任務學習和多模態學習，讓模型學習不同癌症間的知識，並利用這些知識提供精準的癌症預後預測。作為案例研究，我們使用 The Cancer Genome Atlas (TCGA) 計畫所獲得的三個資料集（乳癌、肺癌和大腸直腸癌），實作了用於癌症預後預測的多任務雙模態神經網路，整合 RNA Sequencing (RNA-Seq) 和 Clinical 的資料。除此之外，我們還為此打造一個可重複使用的 Python 程式碼庫，其中包含下載並處理 TCGA 計畫資料庫中的資料、RNA-Seq 資料前處理和深度學習模型開發架構。實驗結果證實，本論文所提出之多任務雙模態神經網路的 concordance index (c-index) 和 area under the precision-recall curve (AUPRC) 分別大幅提高了 26% 和 41%，為此研究方向踏出嶄新的一步。我們相信此研究方向，可以透過深度學習，解開不同癌症之間潛在的關係，為精準醫療奠定更進一步的基礎。 | zh_TW |
| dc.description.abstract | Cancer is one of the leading causes of death worldwide. With accurate cancer prognosis predictions, patients with high risk could be screened out for proper treatments to increase their chance of survival. In this study, we integrate medical data from multiple cancer types and utilize multi-task learning to exploit the shared knowledge among them. As a case study, we implemented the multi-task bimodal neural network, which can handle both RNA-Seq and clinical data, for cancer prognosis predictions with three datasets, including breast, lung, and colon cancer, obtained from the TCGA project. Moreover, we developed a reusable Python code base, including requesting data from the TCGA project database, data pre-processing, and the development pipelines for deep learning models. Experimental results showed significant improvements up to 26% and 41% in the c-index and AUPRC, respectively. Our research marks the initial steps of employing multi-task learning for prognosis predictions among different cancer types. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-09-15T16:18:52Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-09-15T16:18:52Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 i 摘要 ii Abstract iii Contents iv List of Figures vii List of Tables ix Abbreviation xi Chapter 1 Introduction 1 Chapter 2 Data Pre-processing 5 2.1 TCGA Project 5 2.2 GDC mRNA Quantification Analysis Pipeline 6 2.2.1 RNA-Seq Alignment Workflow 7 2.2.2 mRNA Expression Workflow 7 2.3 Data Selection and Summarization 9 2.4 Systems Biology Feature Selection 13 2.4.1 StepMiner and Analysis of Variance (ANOVA) 15 2.4.2 Gene Interaction Network (GIN) and Ranking 16 Chapter 3 Materials and Methods 18 3.1 Deep Learning Model Basics 18 3.1.1 Deep Neural Network 18 3.1.2 Batch Normalization 20 3.2 Bimodal Neural Network 21 3.2.1 Feature Extractor 22 3.2.2 Classifier 25 3.3 Multi-Task Learning 27 3.3.1 Problem Formulation 28 3.3.2 Different Learning Paradigms 28 3.3.3 Hard Parameter Sharing 30 3.3.4 Feature Transformation Approach 32 3.3.5 Optimization 33 3.4 Evaluation Metrics and Methods 34 3.4.1 Receiver Operating Characteristic (ROC) Curve 34 3.4.2 Precision-Recall Curve (PRC) 34 3.4.3 Youden Index 35 3.5 Survival Analyses 36 3.5.1 C-index 36 3.5.2 Kaplan-Meier (KM) and Nelson-Aalen (NA) Analysis 36 3.5.3 Log-Rank Test 38 Chapter 4 Experiments 39 4.1 Experiment Settings 39 4.2 Single-Task Learning 40 4.3 Multi-Task Learning 43 4.4 Survival Analysis 45 Chapter 5 Discussion 5.1 Different Modalities in Single-Task Learning 49 5.2 Different Normalizations for RNA-Seq Data in Single-Task Learning 51 5.3 Multi-Task Learning versus Single-Task Learning 55 5.4 Ablation Studies in Multi-Task Learning 56 5.4.1 Without Ordered RNA-Seq Data 56 5.4.2 Unique RNA-Seq Feature Extractor Without Parameter Sharing 57 5.4.3 Without Task Descriptor 59 5.4.4 Without Weighted Random Data Sampler 60 5.5 Relationships Between Different Cancers in Multi-Task Learning 61 5.6 Bimodal Neural Network Explanation 63 5.6.1 Feature Importance 63 5.6.2 Feature Embeddings 65 5.7 Future Works 66 5.7.1 More Datasets and Modalities 66 5.7.2 Feature Extraction for RNA-Seq Data 67 5.7.3 Definition of Task 68 Chapter 6 Conclusion 70 Bibliography 72 | - |
| dc.language.iso | en | - |
| dc.title | 整合核糖核酸測序與臨床資料並應用多任務學習方法於多癌症預後預測 | zh_TW |
| dc.title | Multi-Cancer Prognosis Prediction with Multi-Task Learning Integrating RNA Sequencing and Clinical Data | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 110-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 李祈均 | zh_TW |
| dc.contributor.oralexamcommittee | Wei-Chung Wang;Mone-Wei Lin;Shao-Hua Sun;Chi-Chun Lee | en |
| dc.subject.keyword | 多任務學習,多模態學習,深度學習,生物資訊學,特徵選取, | zh_TW |
| dc.subject.keyword | multi-task learning,multimodal learning,deep learning,bioinformatics,feature selection, | en |
| dc.relation.page | 87 | - |
| dc.identifier.doi | 10.6342/NTU202203345 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2022-09-26 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 電信工程學研究所 | - |
| dc.date.embargo-lift | 2027-09-02 | - |
| 顯示於系所單位： | 電信工程學研究所 | |
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