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系統識別號 U0026-1208201519511000
論文名稱(中文) 利用巨量生醫文獻探勘進行跨文件關聯擷取應用於老藥新用之研究
論文名稱(英文) Large-Scale Biomedical Literature Mining for Cross-Document Relation Extraction toward Drug Repurposing
校院名稱 成功大學
系所名稱(中) 資訊工程學系
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 103
學期 2
出版年 104
研究生(中文) 朱浚煌
研究生(英文) Jiun-Huang Ju
學號 p78981273
學位類別 博士
語文別 英文
論文頁數 73頁
口試委員 指導教授-蔣榮先
口試委員-呂宗學
口試委員-李宗儒
召集委員-林昭維
口試委員-高宏宇
口試委員-張俊彥
口試委員-楊士德
口試委員-劉宗霖
口試委員-蔡正發
中文關鍵字 巨量資料  文件分流  老藥新用  資訊圖表  蛋白質交互作用  關聯擷取  文件探勘 
英文關鍵字 Big data  Document triage  Drug repurposing  Infographics  Protein interaction  Relation extraction  Text mining 
學科別分類
中文摘要 疾病的產生常常是因為人體內的基因發生突變而導致的,例如許多癌症發生即是因為人體內正常的基因受到誘導或突變而形成所謂的致癌基因,進而使其所在的細胞從正常的生長狀態轉化成腫瘤。另一種情況是人體內保護正常細胞使其避免癌變的腫瘤抑制基因發生突變或功能失常,而導致正常細胞失去其保護機制。利用藥物來治療疾病的眾多關鍵之一即是透過藥物對基因產物進行互動而產生療效,由此可知,藥物、基因、疾病三者之間有著密不可分的關係。一種藥物從研發測試到投入市場往往得花上的數億美元的研發資金及十數年的開發時間。若我們能為已通過人體試驗的核准藥物找出它們的新療效,這將可能大幅縮減開發藥物所需的時間與金錢,而這就是老藥新用。從現有的生醫文獻中找出藥物的潛在新療效是近年來的常見的做法,然而公開的生醫文獻每年都以爆炸性的速度持續增加當中。因此,本研究之目的為從巨量生醫文獻中擷取與分析藥物、基因、疾病三者之間的關聯,進而探索藥物可能之新療效。
在本論文中,我們針對基因產物間複雜的交互作用提出一個以蛋白質語意相似度為基礎之辨識方法來找出全新的交互作用。此外,對於為數龐大的巨量生醫文獻我們提出一個利用資訊檢索特徵之機器學習方法來進行文件分流,以有效降低目標文件之數量並增進搜尋重要文件的效率。最後,我們利用自然語言處理方法在生醫文獻中推論出藥物與疾病的潛在關聯,並對特定癌症進行文獻與臨床之實證分析以說明本研究以資訊技術對老藥新用之實踐。
英文摘要 Diseases are generally caused by the mutations of genes in human body. For example, the oncogene, a normal gene that is abnormally mutated, transforms normal cells into tumors. On the other hand, the mutation of a tumor suppressor gene, a gene preventing a normal cell from being a tumor, leads normal cells to dysfunction. The role of a drug in disease treatment is basically to deal with the mutations of genes, therefore; drugs, genes, and diseases are closely bound up with each other. The drug development takes up to hundreds of millions of U.S. dollars and more than ten years to be on the market. Plenty of time and cost can be reduced if we are able to reposition approved drugs by the use of existing resources, that is, drug repurposing. Though drug repurposing could play an important role in the future drug development and disease therapeutics, the complicated biology mechanism and the popular information technology lead to the rapid growth of the publicly-accessed biomedical resources. Hence, the objective of this research is to extract the relationships between drugs, genes, and diseases to further explore the new indications of drugs.
In this dissertation, we proposed a novel method to identify protein-protein interactions through semantic similarity measures among protein mentions. Moreover, we shrunk a large volume of biomedical literature by a machine learning approach with features generated using information retrieval techniques to facilitate finding important documents. Finally, we utilized natural language processing methods for inferring indirect drug-disease relationships from large-scale biomedical literature and confirmed the suitability of drug candidates identified for repurposing as anticancer drugs by conducting a manual review of the literature and the clinical trials.
論文目次 摘 要 i
ABSTRACT ii
ACKNOWLEDGEMENT iii
CONTENTS iv
LIST OF TABLES vii
LIST OF FIGURES viii
Chapter 1. INTRODUCTION 1
1.1 Motivation 1
1.2 Objective 2
1.3 Organization of Dissertation 3
Chapter 2. RELATED WORKS 4
2.1 Tools for Text Mining 4
2.2 Models for Relation Extraction 5
2.3 Databases for Relation Inference 6
Chapter 3. IDENTIFICATION OF NOVEL PROTEIN-PROTEIN INTERACTIONS 8
3.1 Background 8
3.2 Materials and Methods 10
3.2.1 Page count based semantic similarity (PCBSS) 12
3.2.2 Relation-words reinforcement (PCBSS-RWR) 14
3.2.3 Classification 15
3.3 Experiments and Results 16
3.3.1 Datasets 16
3.3.2 Evaluation metrics 16
3.3.3 Performance of the proposed approaches 17
3.3.4 Feature ablation 18
3.3.5 Comparison with other approaches 19
3.4 Discussion of the novel PPIs 20
3.5 Summary 22
Chapter 4. DOCUMENT TRIAGE FOR CHEMICAL-GENE-DISEASE INFORMATION 24
4.1 Background 24
4.2 Materials and Methods 26
4.2.1 Pre-processing 26
4.2.2 Named entities recognition (NER) modules 26
4.2.3 Learning-to-rank approach and feature extraction 27
4.3 Experiments and Results 31
4.3.1 Dataset 31
4.3.2 Performance metrics 31
4.3.3 Evaluation of individual features 32
4.3.4 Comparison with other approaches 33
4.3.5 The web application (ARTs) 34
4.4 Summary 35
Chapter 5. EXTRACTION OF INDIRECT DISEASE-DRUG RELATIONSHIPS TOWARD DRUG REPURPOSING 37
5.1 Background 37
5.2 Materials and Methods 39
5.2.1 Target document collection 39
5.2.2 Relationship extraction 41
5.2.3 Repurposed drug prioritization 44
5.3 Experiments and Results 46
5.3.1 Relationship extraction evaluation 46
5.3.2 Drug similarity evaluation 48
5.3.3 Drug repurposing evaluation 50
5.4 Discussion of the Suitability of Candidates 52
5.4.1 Evaluation of literature review 52
5.4.2 Evaluation of ClinicalTrials.gov 56
5.5 Summary 58
Chapter 6. CONCLUSION AND FUTURE WORKS 60
REFERENCES 63
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