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研究生：	余旭鈞 Yu, Hsu-Chun
論文名稱：	自生醫文獻中萃取出蛋白質功能註解之資訊 Discovering Protein Functions from Biomedical Literature
指導教授：	蔣榮先 Chiang, Jung-Hsien
學位類別：	博士 Doctor
系所名稱：	電機資訊學院 - 資訊工程學系 Department of Computer Science and Information Engineering
論文出版年：	2005
畢業學年度：	93
語文別：	英文
論文頁數：	100
中文關鍵詞：	蛋白質功能註解、文件探勘
外文關鍵詞：	bioinformatics, information extraction, text mining
相關次數：	點閱：111 下載：1
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　　隨著基因體研究的快速進展，相關之研究文獻亦呈現爆炸性成長，對於生醫研究者而言，要搜尋他們在研究的基因或蛋白質之相關資訊，也變得越來越困難。在這些生醫研究者所需要的資訊中，蛋白質的功能是極為基本而重要的部份。因此，如何利用數量龐大的生醫文獻，將其中的蛋白質功能相關知識轉換成容易存取的資料庫格式，以達成蛋白質功能註解，遂成為一個極重要的課題。而在此課題中，文件探勘乃是不可或缺的關鍵技術。
　　
　　在本論文中，我們提出了一個由生醫文獻中萃取出蛋白質功能的文件探勘程序。對於此問題，我們也開發了一個文件探勘系統──MeKE。在此文件探勘程序中，我們提出了兩個不同的辨識模型，以辨認正確的蛋白質功能。
　　
　　第一個辨識模型使用以文句比對為基礎的分類方式（sentence alignment-based classification）辨識在文件中描述到的蛋白質功能。我們採用貝氏（Naive Bayes）分類器對句子進行分類，而文句比對則是用來取得分類器所需的特徵。這些特徵是關鍵詞，而不是關鍵字，目的在於更精確表達文句上下文的特徵。
　　
　　第二個辨識模型使用文句樣式探勘（sentence pattern mining）和文句樣式比對（sentence pattern matching）辨識在文件中描述到的蛋白質功能。文句樣式探勘可以擷取在文件中常見的遣辭用字或寫作格式。在本論文的問題上，這些樣式即為文件作者們常用來描述蛋白質功能的格式。而文句樣式比對是以片語為單位進行比對，目的是達到具有容錯性的辨識。
　　
　　我們也參加了一個跟此問題相關的競賽──BioCreAtIvE。在此競賽中，我們合併了上述的兩個辨識模型，以取得兩種方法各自的優點。文句分類的優點是可以完全自動化，而文句樣式比對的優點是精確度高。此一合併之方法也確實可以達到比使用單一方法更佳的效能。
　　
　　在本論文中所提出的文件探勘程序可以用來幫助基因資料庫建構者有效率地註解蛋白質的功能，也可以用來輔助生物學家和醫學研究者快速地在生醫文獻中搜尋蛋白質功能的資訊。

　With the rapid growth of articles about genomics research, it has become a challenge for biomedical researchers to access this ever-increasing quantity of information to understand newest discovery of functions of proteins they are studying. To facilitate functional annotation of proteins by utilizing the huge amounts of biomedical literature and transforming the knowledge into easily accessible database formats, the text mining technique thus becomes essential.

　In this dissertation, we propose a text mining procedure for extracting protein functions from biomedical literature, and we develop a text mining system MeKE for this task as well. In this text mining procedure, two different recognition models are proposed to identify correct protein functions. The first recognition model uses sentence alignment-based classification to recognize protein functions in text, and the second recognition model uses sentence pattern mining and sentence pattern matching.

　When participating in the BioCreAtIvE competition, we combine the above two recognition models to utilize the little-human-effort advantage of the sentence classification approach and the high-precision advantage of the sentence pattern matching approach, and this hybrid method creates great potential for achieving higher performance than that of using either one of the two approaches.
　　
　The proposed text mining procedure can be used to aid database curators in annotating protein functions efficiently, and to assist biologists and medical researchers in searching protein functions from biomedical literature rapidly.

中文摘要　I
Abstract　II
誌謝　III
Table of Contents　IV
List of Figures　VI
List of Tables　VIII
Chapter 1　Introduction　1
　1.1　Motivation　1
　1.2　Applications of Text Mining in the Bioinformatics Field　1
　1.3　Annotation of Protein Functions　2
　　1.3.1　Gene Ontology　2
　　1.3.2　Gene Ontology Annotation　4
　1.4　Problem Description　7
　1.5　Organization of Dissertation　8
Chapter 2　Biomedical Text Mining　9
　2.1　Text Mining　10
　2.2　Recognition of Gene and Protein Names　12
　2.3　Extraction of Protein-Protein Interactions　14
　2.4　Related Competitions and Evaluations　16
Chapter 3　The MeKE Text Mining System　21
　3.1　Overview of the MeKE System　21
　3.2　The Text Mining Procedure　23
　　3.2.1　Preprocessing　25
　　3.2.2　Indexing of Protein Names and GO Terms　25
　　3.2.3　Recognition of GO Term Variants　26
　　3.2.4　Extraction of Co-occurrence Sentences　29
Chapter 4　Sentence Alignment-Based Classification　31
　4.1　Sentence Alignment　32
　4.2　Sentence Classification　33
　4.3　Experimental Results　35
　　4.3.1　Data Set　35
　　4.3.2　Evaluation of GO Term Indexing　35
　　4.3.3　Evaluation of Protein Name Indexing　36
　　4.3.4　Evaluation of Sentence Classification　36
　4.4　Concluding Remarks　39
Chapter 5　Sentence Pattern Mining and Matching　41
　5.1　Phrase Parsing　42
　5.2　Sentence Pattern Mining　45
　5.3　Sentence Pattern Matching　55
　5.4　Experimental Results　55
　　5.4.1　Data Set　55
　　5.4.2　Evaluation of GO Term Indexing　56
　　5.4.3　Evaluation of Protein Name Indexing　59
　　5.4.4　Evaluation of Sentence Pattern Mining　59
　5.5　Concluding Remarks　69
Chapter 6　The Hybrid Method in the BioCreAtIvE Competition　71
　6.1　The BioCreAtIvE Competition　71
　6.2　The Hybrid Method　79
　　6.2.1　Sentence Detection and Indexing　80
　　6.2.2　GO Variant Mining　81
　　6.2.3　Sentence Transform　81
　　6.2.4　Template Screening　83
　6.3　Results and Discussion　84
　6.4　Concluding Remarks　87
Chapter 7　Conclusion and Future Work　88
References　90
References　91
Vita　100
                                    

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