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  1. NTU Theses and Dissertations Repository
  2. 工學院
  3. 醫學工程學研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/15907
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor翁昭旼
dc.contributor.authorWun-Long Hongen
dc.contributor.author洪文龍zh_TW
dc.date.accessioned2021-06-07T17:55:03Z-
dc.date.copyright2012-08-19
dc.date.issued2012
dc.date.submitted2012-08-16
dc.identifier.citation[1]:Human Genome Project, http://www.genome.gov/
[2]:Entrez PubMed, http://www.ncbi.nlm.nih.gov/entrez/query.fcgi
[3]:Knowledge base, http://en.wikipedia.org/wiki/Knowledge_base
[4]:Jhang, Y.D. 'Knowledge Visualization in Biomedical Literatures', National Taiwan University Master Thesis, 2005.
[5]:Lin, C.C. 'Signal Transduction Pathways Construction and Visualization by the TRANSPATH Database', National Taiwan University Master Thesis, 2011.
[6]:Zheng, H.T. ,et al. 'GOClonto: An ontological clustering approach for conceptualizing PubMed abstracts', Journal of Biomedical Informatics, Vol. 43, pp. 31-40, 2010.
[7]:Minoru Kanehisa, et al., 'KEGG: Kyoto Encyclopedia of Genes and Genomes', in Pattern Recognition, Nucleic Acids Research, vol. 28, pp. 27-30, 2000.
[8]:Aoki Kinoshita, et al., 'Gene annotation and pathway mapping in KEGG', Methods in Molecular Biology, vol. 396, pp. 71-79, 2007.
[9]:Ekins, Sean, et al., 'Pathway mapping tools for analysis of high content data.', High Content Screening, vol. 356, pp. 319-350, 2007.
[10]:GeneGo MetaCore http://www.genego.com/
[11]:AmiGO, http://amigo.geneontology.org/cgi-bin/amigo/go.cgi
[12]:MGI, http://www.informatics.jax.org/
[13]:De Martino et al., Technical Report of Data Mining, INVISIP IST-2000-29640, Information Visualisation for Site Planning, WP No2: Technology Analysis, D2.2, 28.2.2002
[14]:Fayyad et al.,〝From data mining to knowledge discovery in databases.〞
AI Magazine,vol.17 ,pp. 37-54,1996.
[15]:Breadth First Search, http://en.wikipedia.org/wiki/Breadth-first_search
[16]:Depth First Search, http://en.wikipedia.org/wiki/Depth-first_search
[17]:Gene Ontology, http://www.geneontology.org/
[18]:Frank Schacherer, et al., 'The TRANSPATH signal transduction database: a knowledge base on signal transduction networks' Bioinformatics, vol. 17, pp. 1053-1057, 2001.
[19]:J. C. Bezdek, et al., 'TRANSPATH : an information resource for storing and visualizing signaling pathways and their pathological aberrations', Nucleic Acids Research, vol. 34, pp. D546-D551, 2006.
[20]:Claudia Choi, et al., 'Consistent re-modeling of signaling pathways and its implementation in the TRANSPATH database', BIOBASE GmbH, vol. 15, pp. 244, 2004.
[21]:TRANSPATH Database http://www.gene-regulation.com/
[22]:Saccharomyces Genome Database, http://www.yeastgenome.org/
[23]:FlyBase, http://flybase.org/
[24]:LingPipe, http://alias-i.com/lingpipe/
[25]:Edit Distance, http://en.wikipedia.org/wiki/Edit_distance
[26]:LingPipe,SentencesTutorial,
http://alias-i.com/lingpipe/demos/tutorial/sentences/read-me.html
[27]:Precision Recall F-measure, http://en.wikipedia.org/wiki/Precision_and_recall
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/15907-
dc.description.abstract近年來由於生物醫學相關的研究迅速的發展,以及資訊科技發達的影響下,
生物醫學界每天都有大量資訊的電子文件刊登於各種不同的期刊上,然而要從數
量龐大且複雜的生物醫學文獻資料中找出特定且對於研究人員有意義的資訊可
以說是非常重要但卻不容易的事情,倘若能減少研究人員對於這些生醫文獻資料
處理所花費的時間,也就是間接的節省人力、物力以及時間,因此,如何從文獻
中擷取出相關的知識將是一個重要的課題。
對於生物醫學文獻的處理以及知識的擷取,若只依賴傳統的文件探勘技術,
是無法讓此研究有效地運行,因此本研究將導入專家彙整的生醫知識資料庫,包
含了Gene Ontology 資料庫以及BIOBASE TRANSPATH 資料庫,藉由生醫知識
資料庫的輔助,進而從生物醫學文獻中擷取相關的資訊並加以處理與呈現,並且
設計一個圖形化使用者介面系統,讓研究人員透過簡單的操作方式,即可萃取出
存在於單篇文獻或某一主題中的重要資訊。最後並以在Pubmed/MEDLINE 中有
關VEGF 基因的醫學文獻以及VEGF 交互作用網路圖來驗證本系統,驗證結果
確實可行,對於研究人員有所幫助。
zh_TW
dc.description.abstractUnder the influence of the rapid development of biomedical research and information technology in these years, biomedical circles have numerous e-documents published in various periodicals everyday; however, it is very important but difficult to find specific and meaningful information for researchers from numerous and complicated biomedical literatures. Therefore, if the processing time of these literatures by the researchers could be reduced, it is to save human resources, material and time indirectly; thus how to extract relevant knowledge from documents will certainly become the key of the problem.
For knowledge and information retrieval process in Biomedical literature, If we make use of traditional data mining techniques without the help of expert knowledge to extract the information in the biomedical literature. It may lead to the study can not be effectively run. Therefore, this study will be imported experts pooled biomedical knowledge database includes the Gene Ontology database and BIOBASE TRANSPATH database. we retrieve relevant information from the biomedical literature by the auxiliary of biomedical knowledge database. Then we process and design a graphical user interface system present these relevant information.This system can help researchers to extract the important information in single literatures or certain topic through easy operation. Finally we verify this system by the biomedical literatures relevant to VEGF gene in PubMed/MEDLINE and VEGF interaction network. The verified results are true and feasible,and helpful to researchers.
en
dc.description.provenanceMade available in DSpace on 2021-06-07T17:55:03Z (GMT). No. of bitstreams: 1
ntu-101-R99548053-1.pdf: 3085982 bytes, checksum: 00093c99c4c05112179c5a107f1a4626 (MD5)
Previous issue date: 2012
en
dc.description.tableofcontents誌謝 I
中文摘要 II
Abstract III
目錄 V
圖目錄 VII
表目錄 IX
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 論文架構 3
第二章 相關研究 4
2.1 Knowledge base 4
2.1.1 Machine-readable knowledge bases 4
2.1.2 Human-readable knowledge bases 4
2.1.3 Knowledge base analysis and design ( also known as KBAD ) 4
2.2 Human-readable knowledge bases的相關應用 5
2.2.1 生醫文獻中的資訊探索相關研究論文 5
2.2.2 生醫知識資料庫視覺化資訊系統 8
2.3 Knowledge Discovery in Database (KDD) 13
2.4 圖形視覺化相關演算法 14
2.4.1 Breadth First Search (BFS) 14
2.4.2 Depth First Search (DFS) 15
第三章 材料與方法 16
3.1 研究材料 16
3.1.1 Gene Ontology Database 16
3.1.2 BIOBASE TRANSPATH Database 18
3.1.3 PubMed Database 19
3.2 研究方法 20
3.2.1 視覺化系統之初步建立 21
3.2.2 視覺化系統及資料庫之問題分析 31
3.2.3 視覺化系統及資料庫之問題解決方案 32
第四章 研究結果 36
4.1 Human-readable knowledge bases應用實作結果呈現 36
4.1.1 Classification by GO_Term 36
4.1.2 Tag GO_term and GOTree Visualization Component 38
4.1.3 Tag Gene_Protein Component 39
4.1.4 Gene Interaction Network Visualization Component 40
4.1.5 Gene Interaction Feedback Component 42
4.2 Knowledge Discovery in Database (KDD)概念的運用 43
4.2.1 Gene or Protein Interaction Extraction Component 43
4.2.2 Gene or Protein Interaction Extraction的結果評估 45
第五章 討論 46
5.1 Interaction Network Visualization評估與討論 46
5.2 Gene or Protein Interaction Extraction實驗 48
5.3 Gene or Protein Interaction Extraction評估與討論 50
第六章 結論與未來展望 51
6.1 結論 51
6.2 未來展望 52
參考文獻 53
dc.language.isozh-TW
dc.subject生醫知識資料庫zh_TW
dc.subject知識視覺化呈現zh_TW
dc.subject資訊擷取zh_TW
dc.subject文件探勘zh_TW
dc.subjectKnowledge visualizationen
dc.subjectText miningen
dc.subjectInformation retrievalen
dc.subjectBiomedical knowledge databaseen
dc.title利用生醫知識資料庫探索文獻中的資訊zh_TW
dc.titleInformation Retrieval in Literature by Biomedical Knowledge Databaseen
dc.typeThesis
dc.date.schoolyear100-2
dc.description.degree碩士
dc.contributor.coadvisor蔣以仁
dc.contributor.oralexamcommittee陳中明
dc.subject.keyword生醫知識資料庫,資訊擷取,知識視覺化呈現,文件探勘,zh_TW
dc.subject.keywordBiomedical knowledge database,Information retrieval,Knowledge visualization,Text mining,en
dc.relation.page54
dc.rights.note未授權
dc.date.accepted2012-08-16
dc.contributor.author-college工學院zh_TW
dc.contributor.author-dept醫學工程學研究所zh_TW
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