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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/123456789/28589
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor翁昭旼(Jau-Min Wong),蔣以仁(I-Jen Chiang)
dc.contributor.authorYang-Ying Lien
dc.contributor.author李彥瑩zh_TW
dc.date.accessioned2021-06-13T00:13:15Z-
dc.date.available2007-07-30
dc.date.copyright2007-07-30
dc.date.issued2007
dc.date.submitted2007-07-26
dc.identifier.citation[1]Jenssen T-K, Lægreid A, Komorowski J, Hovig E(2001). A literature network of human genes for high-throughput analysis of gene expression. Nature Genetics 28,21–28.
[2] Ono,T., Hishigaki,H., Tanigami,A. and Takagi,T (2001) .Automated extraction of information on protein-protein interactions from the biological literature. Bioinformatics,17(2),155–161.
[3] Corney, D. P. A., B. F. Buxton, et al. (2004). BioRAT: extracting biological information from full-length papers. Bioinformatics 20(17), 3206-3213.
[4] A Koike, Y Kobayashi, T Takagi (2003). Kinase Pathway Database: An Integrated Protein-Kinase and NLP-Based Protein-Interaction Resource. Genome Research,1231-1243.
[5] Jung-jae Kim, Zhuo Zhang, Jong C. Park and See-Kiong Ng(2006).BioContrasts: extracting and exploiting protein–protein
contrastive relations from biomedical literature. Bioinformatics 22(5),597-605.
[6] Yakushiji,A., Tateisi,Y., Miyao,Y. and Tsujii,J(2001) .Event extraction from biomedical papers using a full parser. In Proceedings of the sixth Pacific Symposium on Biocomputing (PSB 2001),Hawaii, USA, 408–419.
[7] Nikolai Daraselia, Anton Yuryev, Sergei Egorov , Svetalana Novichkova, Alexander Nikitin and Ilya Mazo (2004). Extracting human protein interactions from
MEDLINE using a full-sentence parser . Bioinformatics 20(5),604-611.
[8] Daniel M. McDonald, Hsinchun Chen, Hua Su and Byron B.Marshall. (2004)Extracting gene pathway relations using a hybrid grammar: the Arizona Relation Parser. Bioinformatics 20(18),3370–3378.
[9] Sampo Pyysalo, Filip Ginter, Tapio Pahikkala, Jorma Boberg, Jouni J¨arvinen, and Tapio Salakoski. (2006 ) Evaluation of two dependency parsers on biomedical corpus targeted at protein-protein interactions. Special edition of the International Journal of Medical Informatics on Natural Language Processing in Biomedicine.
[10] Ding, J., Berleant, D., Xu, J. and Fulmer, A. W. (2003) Extracting biochemical interactions from medline using a link grammar parser. In 15th IEEE InternationalConference on Tools with Artificial Intelligence (ICTAI’03), p. 467.
[11] Yung-Chung Lin, Chin-Lin Peng, Cheng-Yan Kao, Hsueh-Fen Juan ,
Hsuan-Cheng Huang.(2005) ProtExt: A system for protein-protein interaction
extraction from PubMed abstract. Bioinformatics 00(00), 1-7.
[12] S Ahmed, D Chidambaram, H Davulcu, C Baral. (2005) IntEx: A syntactic role driven protein-protein interaction extractor for bio-medical text. Proceedings ISMB/ACL Biolink 2005.
[13]Jing Jiang ,ChengXiang Zhai.(2007) An empirical study of tokenization strategies for biomedical information retrieval. Information Retrieval.
[14]Tsuruoka, Y., Y. Tateishi, et al. (2005). Developing a Robust Part-of-Speech Tagger for Biomedical Text. Advances in Informatics - 10th Panhellenic Conference on Informatics, LNCS.
[15]Park, J.C.(2001)Using combinatory categorial grammar to extract biomedical information .IEEE Intelligent Systems 16(6), 62-67.
[16]J Pustejovsky, J Castano, J Zhang, M Kotecki, B.Robust (2002). Relational parsing over biomedical literature: Extracting inhibit relations. Pacific Symposium on Biocomputing.
[17]Hearst, M.(1999) Untangling text data mining. In Proceedings of ACL 1999, 3–10.
[18]AM Cohen, WR Hersh.(2005)A Survey of Current Work in Biomedical Text Mining.Briefings in Bioinformatics
[19]http://bioinformatics.icmb.utexas.edu/idserve/
[20]Jorg Hakenberg, Ulf Leser , Harald Kirsch , and Dietrich-Rebholz-Schuhmann .(2006)Collecting a large corpus from all of Medline . SMBM 2006.
[21] http://genome.jouy.inra.fr/texte/LLLchallenge/
[22]http://gate.ac.uk/
[23] http://www-tsujii.is.s.u-tokyo.ac.jp/GENIA/tagger/
[24] NIH (1999) Relevant terms used for oncogene expression / pharmacology filters.
[25]Friedman,C., Kra,P. Yu,H., Krauthammer,M. and Rzhetsky,A. (2001) GENIES: a natural-language processing system for the extraction of molecular pathways from journal articles. Bioinformatics, 17 (Suppl. 1), S74–S82.
[26]Joshua M. Temkin and Mark R. Gilder (2003)Extraction of protein interaction information from unstructured text using a context-free grammar. Bioinformatics,19 (16), 2046–2053 .
[27]Jorg Hakenberg, Conrad Plake, Ulf Leser, Harald Kirsch, and Dietrich Rebholz-Schuhmann .(2005) LLL'05 Challenge: Genic Interaction Extraction with Alignments and Finite State Automata. Proc Learning Language in Logic Workshop (LLL'05) at ICML 2005, 38-45.
[28]Daniel Sleator and Davy Temperley .(1991)Parsing English with a Link Grammar. Carnegie Mellon University Computer Science technical report CMU-CS-91-196.
[29]Sampo Pyysalo, Tapio Salakoski, Sophie Aubin and Adeline Nazarenko.(2006) Lexical Adaptation of Link Grammar to the Biomedical Sublanguage: a Comparative Evaluation of Three Approaches. BMC Bioinformatics .
[30] http://www.it.utu.fi/biolg/
[31]Madhyastha, Harsha V and Balakrishnan, N and Ramakrishnan, KR.(2003) Event Information Extraction Using Link Grammar. IEEE 2003.
[32] http://www.ipd.uka.de/~durm/tm/munpex/
[33] http://en.opensuse.org/Welcome_to_openSUSE.org
[34]http://tomcat.apache.org/
[35] C. Nédellec.(2005) Learning Language in Logic - Genic Interaction Extraction Challenge.
[36] Mark A. Greenwood, Mark Stevenson, Yikun Guo, Henk Harkema, Angus Roberts.(2005)Automatically Acquiring a Linguistically Motivated Genic Interaction Extraction System.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/28589-
dc.description.abstract蛋白質和蛋白質間交互作用的資訊在研究分子功能途徑中扮演非常重要的角色,因為蛋白質調控了許多細胞的功能,包括細胞內的訊息傳遞、細胞週期等。現今研究學者藉由閱讀生醫電子期刊以獲得重要資訊,但生醫文獻的數量以非常驚人的速度成長,如果以人工擷取資訊,將會耗費大量的時間和人力。在這篇論文中,我們開發一個可以自動從生醫文獻摘要中擷取蛋白質和蛋白質的交互作用的工具,並提供網頁介面顯示。我們分析鍊結文法剖析器的結果以擷取關鍵字為動詞的蛋白質和蛋白質交互作用資訊,並使用詞組型樣來擷取關鍵字為名詞和形容詞的蛋白質和蛋白質交互作用,或是一些鍊結文法剖析器無法正確剖析的文法。我們歸納鍊結文法的連結成規則,並利用這些規則確認句子中的主詞、受詞、動詞和修飾詞組。由這些句法角色中擷取蛋白質和蛋白質的交互作用。我們的系統以LLL05競賽作評估,取得不錯的效能。zh_TW
dc.description.abstractThe information of protein-protein interaction is important for discovering molecular pathways. Researchers in molecular biology can understand more knowledge about cellular processes, protein functions and protein mechanisms through information about protein-protein interaction. Many researchers access knowledge about protein-protein interaction through abstracts of biomedical literature, but the amount of biomedical literature is enormous and continues to grow at exponential rate. So studying up-to-date papers and getting useful information is an overwhelming task for researchers. We develop a system which automatically extracts protein-protein interactions from biomedical abstracts. We extract protein-protein interactions whose interaction word is verb by analyzing the result of Link Grammar parser .Our system develops a set of rules which derives from linkages of Link Grammar to identify subject、object、verb and modifying phrases of a sentence, and extracts protein-protein interactions from these syntactic roles. Our system also takes the advantages of manual pattern approach. protein-protein interactions whose interaction keyword is a noun or adjective , or some sentences that link grammar parser can’t parse exactly such as title. The system is tested on LLL05 challenge and achieves better performance.en
dc.description.provenanceMade available in DSpace on 2021-06-13T00:13:15Z (GMT). No. of bitstreams: 1
ntu-96-R94548060-1.pdf: 1528716 bytes, checksum: 6372abbc0c0dc8648ec925c13f37ed90 (MD5)
Previous issue date: 2007
en
dc.description.tableofcontents誌謝 …………………………………………………………………….i
中文摘要 ………………………………………………………………ii
英文摘要 …………………………………………………………….iii
目錄 ……………………………………………………………………iv
圖目錄 ………………………………………………………………..vi
表目錄 ……………………………………………………………….vii
第一章 緒論………………………………….……………………1
1.1動機……………………………………………………………….1
1.2目的……………………………………………………………….1
1.3論文架構......………………………………………………….2
第二章 相關文獻………………………………………………….3
2.1相關文獻………………………………………………………….3
2.2自然語言處理…………………………………………………….4
2.3文字探勘………………………………………………………….6
第三章 材料和研究方法………………………………………….8
3.1材料……………………………………………………………….8
3.2系統架構…………………………………………………………14
3.3蛋白質和蛋白質的交互作用資訊擷取…………………………19
3.4系統實作…………………………………………………………41
第四章 結果和討論………………………………………………44
4.1 LLL05競賽………………………………………………………44
4.2 討論…………………………………………………………...44
第五章 結論………………………………………………………47
5.1 結論……………...……………………………………………47
5.2 限制…………………………………………………………...47
5.3 未來的工作…………………………………………………...48
參考文獻…………………………………………………………….49
dc.language.isozh-TW
dc.title使用鍊結文法剖析和詞組型樣以擷取蛋白質和蛋白質間的交互作用zh_TW
dc.titleUsing Link Grammar Parsing and Phrase Patterns to Extract Protein-protein Interactionsen
dc.typeThesis
dc.date.schoolyear95-2
dc.description.degree碩士
dc.contributor.advisor-orcid,蔣以仁(ijchiang@tmu.edu.tw)
dc.contributor.oralexamcommittee陳中明(zhong-ming chen)
dc.subject.keyword蛋白質和蛋白質間的交互作用,文字探勘,鍊結文法,詞組型樣,文獻摘要,zh_TW
dc.subject.keywordprotein-protein interaction,text mining,link grammar,phrase pattern,abstract,en
dc.relation.page53
dc.rights.note有償授權
dc.date.accepted2007-07-28
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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